Model of MIS and Leading Research - National Center …borders.arizona.edu/classes/mis696a/projects/final... · Web viewGlobal text matching for information retrieval. Science, vol

Behavioral Technical

Application

Theory

Rigor

Relevance

Model of MISand

Leading Research

Prepared By:Chris Diller

Hoon Cha Matt Jensen Tom MeservyWei Chang Sidd Kaza Rob Schumaker

Cuiping Chen Jun Liu Wei WeiJeff Correll Ying Liu Ming Yuan

Sanghu Gite Iljoo Kim Mike ZhongJoel Helquist Jian Ma Ling Zhu

MIS 696-A – Readings in MISDr. Jay F. Nunamaker

10 December 2003

Fall 2003 Model of MIS and Leading Research 2

TABLE OF CONTENTS

Introduction 3

Artificial Intelligence 10

Collaboration 24

Database 34

Decision Science 49

Economics of Informatics 59

Human-Computer Interaction 72

Social Informatics 88

Systems Analysis & Design 101

Workflow 109

Acknowledgements 113


INTRODUCTION

PROBLEM DEFINITION

The guiding purpose of this research project was two-fold:

1) Develop a model for the MIS domain;2) Develop a method for characterizing the results of research conducted within the

domain.

RESEARCH OBJECTIVES

The specific goals of this project were to:

A) Build on the prior mappings of the MIS domain (by previous classes);B) Identify the top academic researchers and research papers within the MIS domain;C) Use our model to classify the papers within the MIS domain; andD) Display the landmark events for each discipline in a timeline format.

RESEARCH APPROACH

Our group approached this research problem by carefully considering the existing research

in the MIS domain and attempting to classify the work product into several sub-domains –

which would eventually help us assimilate the many broad facets of academic research.

After poring through the prior art of previous classes, and discussing the topic (at length)

amongst ourselves, we determined that the most suitable sub-domain classifications for this

project were as follows:

- Artificial Intelligence (incl. Information Retrieval, Knowledge Management & Bioinformatics);

- Collaboration;- Database;- Decision Science;- Economics of Informatics;- Human-Computer Interaction;- Social Informatics;- Systems Analysis & Design; and- Workflow.

Classifying the major streams of research in this manner enabled us to effectively divide the

project into discrete sections, which would be managed by smaller sub-groups. This

distribution of labor allowed us to focus within particular arenas of interest and gain a better

understanding of the research in each of the sub-domains (and ultimately, help clarify the


INTRODUCTION

sub-domain’s position and importance within the MIS domain). This improved understanding

allowed us to better identify landmark papers and key researchers in each field – simply by

improving each group member’s focus. (e.g. – we were able to identify and interview a

narrower subset of researchers and information sources, pose more specific questions, and

build more effectively on subsequent information/data collected.)

MODEL DEVELOPMENT

Following our initial data collection efforts, our group was able to develop a graphical model

to help us more accurately characterize the significant research contributions of the key

individuals in each field.

Phase One: The MIS Domain Environment

We began the model development process by evaluating the domain mapping models from

the prior classes. The domain mapping model proposed by the Class of 2002 loosely served

a basis for our work – we believe that this group did an effective job in dividing the MIS

domain, thus we adopted their sub-domain classification for this research project. However,

we did not feel that their graphical representation of the domain environment was as clear

as it could have been – thus we substantially modified their visualization to reflect our

perspectives.


INTRODUCTION

Systems Analysis& Design

ArtificialIntelligence

Database

DecisionSciences

Human-ComputerInteraction

Collaboration

Economics ofInformatics

Engineering

ComputerScience

AppliedMathematics Psychology

Communication

SocialScience

Management

MIS Foundations

MISSub-domains

SocialInformatics

Economics

Figure 1: A Model of the MIS Domain Environment

We intended the “concentric circles” representation of the MIS domain environment (shown

in Figure 1) to convey the following:

- The foundational academic disciplines shown on the outer ring are those that most affect (and are affected by) the given MIS sub-domains shown on the inner ring.

- The outer ring is a circle featuring a gradient fill (originating at the center) and lacking discrete separations between the foundational disciplines because more than one of these fields may affect (or be affected by) a research stream – and the amount of influence varies significantly.

- The inner ring is also a circle with a gradient fill (originating at the center) and lacking discrete separations between sub-domains because of the inherent interrelation between them – illustrating the fact that particular research efforts may focus on a combination of sub-domains.

We do not intend this representation to be exhaustive. However, we believe that this

representation adequately shows the relationship between (and amongst) both the

“foundational disciplines” and the “sub-domains” of research activity within the MIS field.


INTRODUCTION

It is important to note that despite the lack of discrete “boundaries” between the MIS sub-

domains, we will present the results of our research divided into those categories. This was

done deliberately in order to simplify the presentation and avoid the myriad combinatorial

categories that could have been created. Thus, the research results are grouped according

to the sub-domain that was MOST influenced or affected by the particular research product

or individual.

Phase Two: The Characteristics of Research Works

In the second phase of our model-building process, our group developed a classification

model to categorize the TYPE of research conducted within each sub-domain. In other

words, we sought to characterize the nature of the contribution of the research work along

three dimensions:


Application

Theory

Rigor

Relevance

Figure 2: The Research Characterization/Classification Model


INTRODUCTION

- Rigor vs. Relevance: Referring to the utilization of research methods that are

mathematical in nature and generally feature tightly controlled experiments (rigor) …

or the general applicability of the research for practitioners (relevance).

- Technical vs. Behavioral: Referring to the positivist philosophy (technical) … or

interpretivist philosophy (behavioral) that guided the scientific discovery process or

methods employed.

- Theory vs. Application: Referring to the characterization of an environment or set

of conditions (theory) … or the use of theory to develop a usable solution

(application).

It is important to note that our particular use of this model (shown in Figure 2) for

characterizing and classifying the research work in a given domain is somewhat constrained.

Rather than attempting to plot a specific point along each of these continuums in three-

dimensional space for each paper, we simply opted to identify the quadrant that best

characterizes the paper’s contribution.

Therefore, we created another visual representation of the characterization space (shown in

Figure 3) for the purposes of displaying our evaluations of the research works contained this

report. Note that the three-dimensional space is divided into a set of eight cubes, each

representing a different combination of the model characteristic qualities:

- Relevant Behavioral Theory [WHITE]- Relevant Behavioral Application [RED]- Relevant Technical Theory [GREEN]- Relevant Technical Application [BLUE]- Rigorous Behavioral Theory [ORANGE]- Rigorous Behavioral Application [PURPLE]- Rigorous Technical Theory [YELLOW]- Rigorous Technical Application [GREY]


INTRODUCTION


Application

Theory

Rigor

Relevance

RelevantTechnical

Application

RelevantBehavioralApplication

RigorousBehavioralApplication

RigorousTechnical

Application

RigorousBehavioral

Theory

RelevantBehavioral

Theory

RelevantTechnical

Theory

RigorousTechnical

Theory

Figure 3: Alternate Representations of the Research Characterization Space

Note that each of the seminal works highlighted in this paper will feature a graphic

(conspicuously located along the left margin of the page) that corresponds to that paper’s


INTRODUCTION

research contribution characteristics and its corresponding location in the model’s

characterization space.

RESEARCH METHODOLOGY

Once the project was divided into sub-domains and our models completed, our group began

collecting data. Within each sub-domain, we used the following sources to identify key

academic research papers:

- Faculty interviews (incl. face-to-face interviews, telephone surveys, and e-mail questionnaires);

- Prior years' final projects;- Reading lists from related courses at various universities; and- Internet & library database searches.

After a body of candidate works had been collected, we began the process of evaluating and

ordering the various works. The primary methods utilized to “prioritize” the papers

included:

- Faculty opinion;- Citation counts;- Relative journal quality measures; and- Third-party evaluations/critiques of the works. (e.g. – university and/or researcher

websites)

Ultimately, the identification of the key researchers in each area became a natural extension

of prioritization of the seminal works identified in the process above. Some researchers

listed in the project are listed due to their pioneering status, some are listed based upon the

relative magnitude of their effect on the sub-domain, and still others deserved mention due

to the sheer volume of work they have contributed to the sub-domain.

RESEARCH CONTRIBUTIONS

The results of this project can be found on the following pages, arranged alphabetically by

sub-domain.


INTRODUCTION

Artificial Intelligence: Bioinformatics, Knowledge Management, and Information Retrieval

1956McCarthy coins

the term"Artificial Intelligence"

1967Successful

knowledge-basedprogram:

Dendral is built

1972A Model of

Evolutionary Changein Proteins

1990Basic Local

Alignment SearchTool

1995Bayesian method

developed fordetermining atomic

positions

1997Deep Blue

chess programbeats Kasparov

1999EcoCyc: The

Resource and theLessons Learned

2002Accomplishmentsand challengesin literature data

mining for biology

Since publication of Alan Turing’s famous Turing test, AI research has become an active

research domain. Today it encompasses a variety of subfields such as learning and

perception to such specific fields as knowledge management, information retrieval, and

bioinformatics. The field of AI has changed its focus from a human-centric approach to

today’s rationalist approach. A human-centric approach is an empirical science, involving

hypothesis and experimental confirmation. A rationalist approach involves a combination of

mathematics and engineering. Each approach has both disparaged and helped each other.

In general, AI is a branch of science that deals with helping machines find solutions to

complex problems in a more natural or rational way. AI, although closely associated with

Computer Science, is a research domain that links to other fields such as Mathematics,

Psychology, Cognition, Biology, Philosophy, and many others. Over the past five decades, AI

researchers have mostly been focusing on solving specific problems. Numerous solutions

have been devised and improved to do so efficiently and reliably. This explains why the field

of AI is split into so many branches, ranging from pattern recognition to artificial life,

including evolutionary computation and planning. Because of its close relationship with

human beings there are many AI applications used in management information systems.


ARTIFICIAL INTELLIGENCE

Specifically, information retrieval, knowledge management and bioinformatics are three sub

areas that have seen many matured applications of AI research.

Within the context of our model, the key artificial intelligence papers include:

BIOINFORMATICS

Bioinformatics is the application of information systems to the science of biology. These applications include information retrieval, natural language processing and heuristics, to derive new knowledge from data such as DNA and protein sequences.

A Model of Evolutionary Change in ProteinsDayhoff, M. O., Eck, R. V., Park, C. M.Atlas of Protein Sequence and Structure, 5, 1972 [Link]

Model Classification Quadrant: Rigorous, Technical, Theory

This paper talks about the theory of PAM (Point Accepted Mutations). The PAM theory measures the amount of mutation in a protein sequence by counting how far away the original amino acid has moved, also known as the evolutionary distance.

A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data SourcesAltman, R. B.International Journal Human-Computer Studies, 42, 1995 [Link]


This algorithmic paper develops a Bayesian method for determining relative atom positions, and from there plotting probable molecular structures using various model constraints.

Accomplishments and Challenges in Literature Data Mining for BiologyHirschman, L., Park, J. C., and Tsujii, J.BioInformatics Review, 18 (12) 2002 [Link]

Model Classification Quadrant: Relevant, Technical, Theory

The paper reviews the advances and challenges in literature mining. Literature mining has progressed from simple recognition of terms to deriving relationships and interactions between proteins and genes. The paper poses questions that are still unanswered in current research.

Basic Local Alignment Search ToolAltschul, S. F., Gish, W., Miller, W.Journal of Molecular Biology, 215(3), 1990 [Link]

Model Classification Quadrant: Rigorous, Technical, Application

BLAST is a new approach that approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. The algorithm is simple and robust and



http://portal.acm.org/ft_gateway.cfm?id=99985&type=pdf&coll=portal&dl=ACM&CFID=14226019&CFTOKEN=4726321

http://pir.georgetown.edu/pirwww/aboutpir/doc/data_mining.pdf

http://www-smi.stanford.edu/pubs/SMI_Reports/SMI-95-0555.pdf

http://www.md.huji.ac.il/courses/bioinfo01/Refs/Dayhoff.html

can be implemented in many ways. BLAST was an order of magnitude faster than the existing sequence comparison tools of the time.


EcoCyc: The Resource and the Lessons LearnedKarp, P. D., Riley, M.SRI Report, 1999 [Link]

Model Classification Quadrant: Relevant, Technical, Application

This paper talks about the EcoCyc tool, a GUI software device that allows researchers to query and explorer the genetic pathways of E. Coli. The EcoCyc tool also allows scientists to perform metabolic simulations.

INFORMATION RETRIEVAL

Automatic Structuring and Retrival of Large Text FilesGerard Salton, James Allen, Chris BuckleyCommun. ACM 37 (2): 97-108 (1994). [Link]


In the study of this article, he described the procedures for analyzing large text contexture, structuring the text by linking text excepts covering related subject matter, and retrieving text items in response to available user queries.

Natural Language Processing for Information RetrievalJones, K.S.Communications of the ACM, 39, 1, (1996), 92-101 [Link]


This article explores Text Retrieval’s key properties, summarizes past experience in the field and reviews various specific Natural Language Processing research strategies. It gives us good understanding of Information Retrieval and guide line for research in that area.

The Anatomy of a Large-scale Hypertextual Web Search EngineBrin, S. and Page, L.Proceedings of the Seventh WWW Conference, Brisbane, Australia, 1998. [Link #1] [Link #2]


Brin and Page describe the implementation of a search engine for the World Wide Web, including the PAGERANK algorithm, a query-independent measure of document quality based on an analysis of Web links.



http://citeseer.nj.nec.com/brin98anatomy.html

http://www-db.stanford.edu/pub/papers/google.pdf

http://www-db.stanford.edu/pub/papers/google.pdf

http://portal.acm.org/citation.cfm?id=234210&coll=ACM&dl=ACM&CFID=7869154&CFTOKEN=52431065

http://portal.acm.org/citation.cfm?id=175235.175243&dl=portal&dl=ACM

http://citeseer.nj.nec.com/cache/papers/cs/1742/http:zSzzSzecocyc.pangeasystems.comzSzecocyczSzdoczSzecocyc99.pdf/karp99ecocyc.pdf

KNOWLEDGE MANAGEMENT

CBR in Context: The Present and FutureLeake, David B.AAAI Press/MIT Press, 1996 [Link]


It provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches, and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research."

Dendral and Meta-dendral: Roots of Knowledge Systems and Expert System ApplicationsFeigenbaum E. A. and Buchanan B. G.Artificial Intelligence 59, 1993, 233-240 [Link]


The significance of DENDRAL system was that it was the first successful knowledge-intensive system. The success of DENDRAL was instrumental in convincing the AI research community of importance of knowledge representation. The DENDRAL program was an existence proof that computers could couple technical knowledge with simple inference mechanisms.

Design, Analogy, and CreativityGoel, Ashok K.IEEE Expert/Intelligent Systems & Their Applications, Vol. 12, No.3, 1997. [Link]


Analogical reasoning appears to play a key role in creative design. This article briefly reviews recent AI research on analogy-based creative design before enumerating a related set of research issues.

What are Ontologies, and Why Do We Need Them?Chandrasekaran, B., Josephson, J., and Benjamins, R.IEEE Intelligent Systems 14(1), 1999 [Link]


Much current knowledge representation research develops sharable ontologies that represent particular domains. Ontologies provide a formal specification of the concepts in the domain and their relationships, to use as a foundation for developing knowledge bases.



http://ieeexplore.ieee.org/iel4/5254/16144/00747901.pdf?isNumber=16144&prod=JNL&arnumber=747901&arSt=18&ared=19&arAuthor=Swartout,+W.;+Tate,+A.

http://citeseer.nj.nec.com/goel97design.html

http://ejournals.ebsco.com/Article.asp?ContributionID=2396724

http://citeseer.nj.nec.com/22268.html

OTHERS

A Framework for Representing Knowledge, Psychology of Computer Vision Minsky, MarvinSemantic Information Processing, MIT Press, Cambridge, MA, 1968, pages 403-418 [Link]


A major influence in persuading AI workers and psychologists to consider representing commonsense knowledge in relatively large structures called "frames," which exemplify typical instances or cases. Frames inherit default assumptions that can be displaced when more specific information is available.

Programs with Common Sense McCarthy, JohnSemantic Information Processing, MIT Press, Cambridge, MA, 1968 [Link]


Arguably the first paper on logical AI (i.e. AI in which logic is the method of representing information in computer memory and not just the subject matter of the program). This paper discusses programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements. The basic program will draw immediate conclusions from a list of premises. These conclusions will be either declarative or imperative sentences.

The Science of the ArtificialSimon, Herb A.2nd Edition, MIT Press, Cambridge, MA, 1981 [Link]


This is the paper that investigates and defines the methodology status of AI research. It takes into account important advances in cognitive psychology and the science of design while confirming and extending its basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action.



http://citeseer.nj.nec.com/context/25230/0

http://citeseer.nj.nec.com/mccarthy68programs.html

http://citeseer.ist.psu.edu/context/6446/0

CONTACT INFORMATIONDepartment of GeneticsStanford University Medical Center300 Pasteur Drive, Lane L329, Mail Code: 5120Stanford, CA 94305-5120(650) 725-3394 [email protected]://smi-web.stanford.edu/people/altman/

EDUCATIONM.D. – Stanford Medical School, 1990Ph.D. – Stanford Program in Medicine Information Sciences, 1989AB – Harvard College, 1983

RESEARCH INTERESTSAnalysis of protein and RNA structure and function, Novel user interfaces to biological data, and Analysis of microenvironment to the structure of important biological macromolecules.

KEY PUBLICATIONS Mooney, S.D., Altman R.B., MutDB: annotating human variation with functionally relevant

data, Bioinformatics, 19(14), 1858-60. Liu S., Altman R.B., Large scale study of protein domain distribution in the context of

alternative splicing, 31(16), 4828-35. Peleg M., Yeh I., Altman R.B., Modeling biological processes using workflow and Petri Net

models, Bioinformatics, 18(6), 825-37.


Russ Biagio Altman– Associate Professor of Genetics, Medicine and

Computer Science– Director of the Medical Informatics Training

ProgramDepartment of GeneticsStanford University Medical Center (Stanford, CA)


http://smi-web.stanford.edu/people/altman/

CONTACT INFORMATIONKnowledge Systems LaboratoryDepartment of Computer ScienceGates Computer Science Building, 2AStanford UniversityStanford, CA 94305-9020(650) 723-3444 [email protected]://ksl-web.stanford.edu/people/eaf/

EDUCATIONPh.D. – Carnegie Mellon UniversityBS – Carnegie Mellon University

RESEARCH INTERESTSKnowledge-Based Systems Research & Applications; Computer Industry Research; Defense Technology and Technology Policy

KEY PUBLICATIONS Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, Joshua Lederberg:

DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation. Artif. Intell. 61(2): 209-261 (1993).

Edward A. Feigenbaum: Tiger in a Cage: The Applications of Knowledge-based Systems (1993) - Abstract. AAAI 1993.


Edward Feigenbaum– Professor of Computer Science– Co-Scientific Director, Knowledge Systems

LaboratoryStanford University (Stanford, California)


http://ksl-web.stanford.edu/people/eaf/

mailto:[email protected]

http://www.ksl.stanford.edu/people/eaf/ed.jpg

Edward A. Feigenbaum, Peter E. Friedland, Bruce B. Johnson, H. Penny Nii, Herbert Schorr, Howard E. Shrobe, Robert S. Engelmore: Knowledge-Based Systems Research and Applications in Japan, 1992. AI Magazine 15(2): 29-43 (1994).

James A. Hendler, Edward A. Feigenbaum: Knowledge Is Power: The Semantic Web Vision. Web Intelligence 2001: 18-29.

TRIVIA / MISCELLANEOUS– Chief Scientist of the United States Air Force (1994-1997)– Past President of the American Association for Artificial Intelligence.

CONTACT INFORMATIONColumbia University622 West 168 St, VC-5New York, NY 10032 (212) 305-5780 [email protected]://www.dbmi.columbia.edu/~friedma/

EDUCATIONPh.D. – Ph.D. Computer Science, New York University, 1989MA – Computer Science, New York University, 1986BS – Mathematics, City College CUNY, 1962

RESEARCH INTERESTSNatural language text processing, Clinical knowledge representation, & Database management systems.

KEY PUBLICATIONS Friedman C, Kra P, Krauthammer M, Yu H, Rzhetsky A. GENIES: a natural-language

processing system for the extraction of molecular pathways from journal articles, Bioinformatics, 2001, suppl1 S74-82.

Rzhetsky A, Koike T, Kalachikov SM, Gomez M, Krauthammer SH, Kaplan P, Kra P, Russo JJ, Friedman C. A knowledge model for analysis and simulation of regulatory networks. Bioinformatics, 2000, 16(12), 1120-28.


Carol Friedman- Professor, Department of Medical InformaticsColumbia University (New York, New York)


http://www.dbmi.columbia.edu/~friedma/


http://www.dbmi.columbia.edu/homepages/friedma/

Friedman C, Cimino JJ, and Johnson SB. A schema for representing medical language. Journal of American Medical Informatics Association, May 1994, 1(3), 233-248.


CONTACT INFORMATION3721 Executive Center DriveSuite 100Austin, TX 78731(512) 342-4000 [email protected]://www.cyc.com/staff.html

EDUCATIONPh.D. – Computer Science, Stanford, 1976MS – Applied mathematics, University of Pennsylvania, 1972BA – Mathematics and Physics, University of Pennsylvania, 1970

RESEARCH INTERESTS Large common sense knowledge base which may reduce expert systems' brittleness, enable natural language systems' semantic disambiguation, and facilitate learning by analogy.

KEY PUBLICATIONS Douglas B. Lenat: CYC: A Large-Scale Investment in Knowledge Infrastructure. Commun.

ACM 38(11): 32-38 (1995).


Douglas B. Lenat- President and CEO CycCorpCycCorp Inc. (Austin, Texas)


http://www.computerworld.com/computerworld/records/images/story/15TFWmemome_lenat_secondary.jpg

Douglas B. Lenat, Edward A. Feigenbaum: On the Thresholds of Knowledge. Artif. Intell. 47(1-3): 185-250 (1991).

Douglas B. Lenat: Correction to "Ontological Versus Knowledge Engineering". IEEE Trans. Knowl. Data Eng. 1(3): 410 (1989).


CONTACT INFORMATIONRoom 208, Gates Building 2AComputer Science DepartmentStanford UniversityStanford, California 94305-9020(650) 723-4430 [email protected]://www-formal.stanford.edu/jmc/index.html

EDUCATIONPh.D. – Mathematics, Princeton University, 1951B.S. – Mathematics, California Institute of Technology, 1948

RESEARCH INTERESTSHis main artificial intelligence research area has been the formalization of common sense knowledge.

KEY PUBLICATIONS John McCarthy, “Recursive Functions of Symbolic Expressions and Their Computation by

Machine, Part I”, CACM, April 1960. John McCarthy, “Programs with Common Sense”, Semantic Information Processing, MIT

Press, Cambridge, MA, 1968, pages 403--418. John McCarthy, Pat Hayes, "Some Philosophical Problems from the Standpoint of

Artificial Intelligence”, Machine Intelligence 4, 1969. John McCarthy, “First Order Theories of Individual Concepts and Propositions.” Machine

Intelligence, 1986.


John McCarthy- Professor EmeritusComputer Science Department Stanford University (Stanford, California)


http://www-formal.stanford.edu/jmc/index.html


http://www.cs.uga.edu/

http://www-formal.stanford.edu/jmc/jmccolor.jpg

TRIVIA / MISCELLANEOUSSometimes John is called the “Father of AI”

John invented the LISP programming language in 1958, developed the concept of time-sharing in the late fifties and early sixties, and has worked on proving that computer programs meet their specifications since the early sixties.

John invented the circumscription method of non-monotonic reasoning in 1978.


CONTACT INFORMATIONNational Library of Medicine8600 Rockville PikeBethesda MD 20894(301) 496-4441 [email protected]://lhncbc.nlm.nih.gov/od/staff/mccray_alexa/

EDUCATIONPh.D. – Georgetown University, 1981

RESEARCH INTERESTS Natural Language Processing, classification methods, intelligent navigation, knowledge representation

KEY PUBLICATIONSMcCray, Alexa T., Gallagher, Marie E. (2001). Principles for digital library development, CACM, 44(5).


Alexa McCray – Director Lister Hill National Center for Biomedical

CommunicationNational Library of Medicine (Bethesda, Maryland)



CONTACT INFORMATION

The Media LaboratoryBuilding E1577 Massachusetts AvenueCambridge, MA 02139-4307617-253-5960 [email protected]://web.media.mit.edu/~minsky/

EDUCATION

Ph.D. – Mathematics, Princeton University, 1954BA – Mathematics, Harvard University, 1950

RESEARCH INTERESTS

AI, cognitive psychology, mathematics, computational linguistics, robotics, and optics. In recent years, he has worked chiefly on imparting to machines the human capacity for commonsense reasoning.

KEY PUBLICATIONS

Minsky, Marvin, “Perceptrons: An Introduction to Computational Geometry”, The MIT Press, (1969).

Minsky, Marvin, “A framework for representing knowledge, Psychology of Computer Vision”, ed. P. Winston, McGraw Hill, (1975).

Minsky, Marvin, ”K-lines, A theory of memory”, Cognitive Science, 4, 1980 pp117-133.

TRIVIA / MISCELLANEOUSMarvin was awarded the 1969 Turing Award.


Marvin Minsky- Toshiba Professor of Media Arts and Sciences- Professor of E.E. and C.S.Massachusetts Institute of Technology (Cambridge, MA)


http://web.media.mit.edu/~minsky/


INFORMATIONMemorial website: http://www.cs.cornell.edu/Info/Department/Annual95/Faculty/Salton.html

EDUCATIONPh.D. – Computer Science, Harvard University, 1958MA – Mathematics, Brooklyn College, 1950BA – Mathematics, Brooklyn College, 1952

RESEARCH INTERESTSNatural-language processing, especially information retrieval, SMART information retrieval system in the 1960s (allegedly, SMART is known as "Salton's Magical Automatic Retriever of Text") as the main research tool.

KEY PUBLICATIONS Salton, G. Automatic information retrieval. Computer, vol. 13, 5, 1980, 41-57. Salton, G., H. Wu, and C. T. Yu. The measurement of term importance in automatic

indexing. Journal of the ASIS, vol. 32, 3, 1981, 175-186. Salton, G., C. T. Yu, and K. Lam. Term weighting in information retrieval using the term

precision model. Journal of the ACM 29, 1, 1982, 152-170. Salton, G., and M. J. McGill. Intro to Modern Information Retrieval. New York: McGraw-Hill,

1983. Salton, G., E. A. Fox, and H. Wu. Extended Boolean information retrieval.

Communications of the ACM, vol. 26, 11, 1983, 1022-1036. Salton, G. Automatic Text Processing. Reading, MA: Addison Wesley, 1989.


Gerard Salton (1927-1995)– Professor (1965-1995)Computer Science DepartmentCornell University (Ithaca, New York)


http://www.cs.cornell.edu/Info/Department/Annual95/Faculty/Salton.html

Salton, G. and C. Buckley. Global text matching for information retrieval. Science, vol. 253, 1991, 1012-1015.

TRIVIA / MISCELLANEOUSGerard taught at Harvard University (1958 – 1965), was associate editor of the ACM Transactions on Information Systems, and was a fellow for the Association for Computing Machinery.

Program Committee: SIGIR-95 Eighteenth International Conference on Research and Development in Information Retrieval Seattle 1995; SIGIR-96 Nineteenth International Conference on Research and Development in Information Retrieval Zurich, Switzerland, 1996.

INFORMATIONMemorial website: http://www.psy.cmu.edu/psy/faculty/hsimon/hsimon.html

EDUCATIONPh.D. – Political Science, University of Chicago, 1943BA – Political Science, University of Chicago, 1936

RESEARCH INTERESTS AI, information Processing systems, intelligence & epistemology, social implications, Economics & management, and the Philosophy of science.

KEY PUBLICATIONS Herbert A. Simon, “The science of Artificial”, MIT Press, 3rd edition, 10, 1996 Newell, A., Shaw, J.C., & Simon, H.A. (1959). Report on a general problem-solving

program [Abstract]. Communications of the Association for Computing Machinery, 2, 19.

Simon, H.A. (1968). Administrative behavior. In D.L. Sills (Ed.), International encyclopedia of the social sciences (Vol. 1, pp. 74-79). New York, NY: Macmillan and The Free Press.

Simon, H.A. (1980). Cognitive science: The newest science of the artificial. Cognitive Science, 4, 33-46.


Herbert A. Simon (1916-2001)– ProfessorComputer Science, Psychology, Social & Decision

Science, and Industrial Administration Departments

Carnegie Mellon University (Pittsburgh, Pennsylvania)


http://www.psy.cmu.edu/psy/faculty/hsimon/hsimon.html

TRIVIA / MISCELLANEOUSHerb was awarded the Nobel Prize (for his work in Economic Sciences) in 1978.


Collaboration

1971Delphi method

proposed as meaningfulgroup communication structure.

1987Collab usedat Xerox labs

1987Foundation forstudy of GSS

1991Communicationrequirements

for groupsupport established

1991Benfits and

drawbacks ofGSS established 1996

Groupware Grid proposed

Collaboration technologies are a specific type of information system that facilitates the

group work process. Group Support Systems (GSS) and Group Decision Support Systems

(GDSS) are two fundamental components of collaboration technologies. The aim of GSS and

GDSS technologies is to make the group work process more efficient by providing group

memory, anonymity, and parallel communication so that time is better utilized (Nunamaker

et al., 1991). Collaboration technologies provide the support and structure to allow group

work to take place in a more efficient and effective manner.

Research in the collaboration domain includes both behavioral and technical aspects. The

behavioral research looks at such things as the dynamics of human communication and

interaction in group work. The technical research includes such things as the development

of information systems tools and frameworks to facilitate the group work. Collaboration

research combines aspects from other major research fields, including decision sciences and

communications.


COLLABORATION

Within the context of our model, the key collaboration papers include:

Electronic Brainstorming and Group SizeGallupe, Brent R., Dennis, Alan R., Cooper, William H., Valacich, Joseph, Bastianutti, Lana M., Nunamaker Jay F.Academy of Management Journal, 35(5), 1992, 350-369. [Link]

Model Classification Quadrant: Rigorous, Behavioral, Application

Two experiments were conducted with small and large groups. The quality of ideas was compared between the groups. The larger groups generated more unique, high quality ideas and the members were more satisfied when electronic brainstorming was used. There were few differences for smaller groups. Electronic brainstorming reduces the effects of production blocking and evaluation apprehension.

Issues in the Design of Group Decision Support SystemsHuber, G.P. MIS Quarterly, 4, 1984, 195-204. [Link]

Model Classification Quadrant: Rigorous, Behavioral, Theory

The need for GDSS systems is driven by the clash of two forces: The environmentally-imposed demand for more information sharing and the resistance to allocating more managerial attention and professional time to sharing information. In addressing this clash, the paper focuses on three issues in the design of these systems: System capabilities, system delivery modes, system design strategies. Each of these issues has a connection to the system’s survival.

Electronic Meeting Systems to Support Group WorkNunamaker, Jay F., Dennis, Alan R., Valacich, Joseph S., Vogel, Douglas R., George, Joey F.Communications of the ACM, 34(7), 1991, 40-61. [Link]

Model Classification Quadrant: Relevant, Behavioral, Application

This paper provides an introduction to electronic meeting systems. Advantages of the system include the fact that all participants work simultaneously, all have an equal opportunity for participation, counterproductive behavior is discouraged, larger groups can work together effectively, outside information is easily accessible and an automatic organizational memory is generated. Disadvantages are that anonymity may mean that individuals may not participate at all, reaction to comments is slowed by the mechanics of typing, there is less richness in the communication process and separation of people from comments can lead to a feeling of dehumanization among participants.

Beyond the Chalkboard: Computer Support for Collaboration and Problem Solving in MeetingsStefik, M., Foster, G., Bobrow, D.G., Kahn, K., Lanning, S. & Suchman, L.Communications of the ACM, 30(1), 1987, 33-47. [Link]



COLLABORATION

http://delivery.acm.org/10.1145/10000/7887/p32-stefik.pdf?key1=7887&key2=1821439601&coll=GUIDE&dl=ACM&CFID=14298477&CFTOKEN=17454095

http://infotrac.galegroup.com/itw/infomark/660/721/67696075w3/purl=rc1_EAIM_0_A10966728&dyn=12!xrn_25_0_A10966728?sw_aep=uarizona_main

http://search.epnet.com/direct.asp?an=4679575&db=aph&site=ehost

http://links.jstor.org/sici?sici=0001-4273(199206)35:2%3C350:EBAGS%3E2.0.CO;2-Q

An experimental meeting room called the Colab at Xerox PARC has been created to study computer support of collaborative problem solving in face-to-face meetings. The long-term goal is to understand how to build computer tools to make meetings more effective.

Delphi and its Potential Impact on Information SystemsTuroff, M.AFIPS Conference Proceedings, Fall Joint Computer Conference, 39, 1971, 317-326. [Link]


This work proposes that the Delphi method can be used to establish a meaningful group communication structure. The paper illustrates that the Delphi method is “a method for the systematic solicitation and collation of informed judgments on a particular topic”

Lessons from a Dozen Years of Group Support Systems Research: A Discussion of Lab and Field FindingsNunamaker, J.F., Jr.; Briggs, R.O.; Mittleman, D.D.; Vogel, D.R.; and Balthazard, P.A.Journal of Management Information Systems, 13, 3 (Winter 1996-97), 163-207. [Link]


A theoretical foundation for the Groupware Grid, a tool for designing and evaluating GSS, is presented. Lessons are presented from 9 key domains: 1. GSS in organizations, 2. cross-cultural and multicultural issues, 3. designing GSS software, 4. collaborative writing, 5. electronic polling, 6. GSS facilities and room design, 7. leadership and facilitation, 8. GSS in classroom and 9. business process reengineering.

Computer Mediated Communication Requirements for Group SupportTuroff, MurrayJournal of Organizational Computing, Volume 1, Number 1. (1991), 85-113. [Link]


This article provides an overview of historical evolution of computer mediated communication systems within the context of designing for group support. A number of examples of design features to support specific group tasks are illustrated. The result of this is a synthesis of a number of observations on the assumptions and goals for the design of CMC systems. An emphasis is placed on the advantages offered by asynchronous support of communication process, self-tailoring of communication structures by users and groups, and the integration into the communication system of other computer resources and information systems.

Information Technology for Negotiating Groups: Generating Options for Mutual GainNunamaker, J. F., Jr., Dennis, Alan R., Valacich, Joseph S., Vogel, Douglas R.Management Science. Linthicum: Oct 1991. Vol. 37, Iss. 10; pg. 1325-47. [Link]



COLLABORATION

http://links.jstor.org/sici?sici=0025-1909(199110)37:10%3C1325:ITFNGG%3E2.0.CO;2-V

http://eies.njit.edu/~turoff/Papers/CMCGS.pdf

http://proquest.umi.com/pqdweb?index=6&did=000000011475016&SrchMode=3&sid=1&Fmt=4&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1069342255&clientId=43922

http://eies.njit.edu/~turoff/Papers/DelphiImpact.pdf

This paper addressed the important initial stage of negotiation: generating options for mutual gain. An integrated series of laboratory and field studies is presented that investigated various aspects of computer-supported option generation for groups that meet at the same place and time. The use of anonymity to separate personalities from the issues and promote more objective evaluation was found to improve option generation in some circumstances, particularly those with increased criticalness or power differences among the participants. Larger groups were found to be more effective than smaller groups, several smaller groups combined, and nominal groups.

Future Research in Group Support Systems: Needs, Some Questions and Possible DirectionsNunamaker, J. F.International Journal of Human-Computer Studies, 47, 3, 1997, 357-385. [Link]


This paper discusses the future of GSS research in terms of what is needed, some important research questions, and offers some possible directions. Section 1-4 describes the fundamental background information for GSS research, the need for GSS research, the multi-methodological approach and several major issues in applying GSS in organizational settings. Section 5-7 explores important GSS questions, and keys to successful distributed collaboration from our experience and provide some answers to the difficult question "what is needed for a distributed workspace?" Section 8-10 clarifies just what virtual reality can offer for distributed collaboration, the justification for a virtual reality representation of the distributed office and what we need to get real work done in a virtual workspace including: support for sense making during the process, automating bottlenecks in the process, modeling through simulation and animation, multiple languages, education, crisis response and software inspection.

Effects of anonymity and evaluative tone on idea generation in computer-mediated groupsConnolly, Terry, Jessup, Leonard M., Valacich, Joseph S. LinthicumManagement Science Jun 1990, 36, 6, p. 689 – 703. [Link]

Model Classification Quadrant: Relevant, Behavioral, Theory

The effects of anonymity and evaluative tone on computer-mediated groups were evaluated using a group decision support system to perform an idea-generation task. Evaluative tone was manipulated through a confederate group member who entered supportive or critical comments into the automated brainstorming system. Groups working anonymously and with a critical confederate produced the greatest number of original solutions and overall comments, but average solution quality per item and average solution rarity did not differ across conditions. Identified groups working with a supportive confederate were the most satisfied and had the highest levels of perceived effectiveness, but they produced the fewest original solutions and overall comments.

Groupware: Some Issues and ExperiencesEllis, Clarence A., Gibbs, Simon J., Rein, Gail L.Communications of the ACM, Jan 1991, 34, 1, pp. 38-59. [Link]

Model Classification Quadrant: Relevance, Application, Technical

The goal of groupware is to assist groups in communicating, in collaborating, and in coordinating their activities. The most familiar example of groupware is the computer-based


COLLABORATION



http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WGR-45M91KT-C-1&_cdi=6829&_orig=browse&_coverDate=09/30/1997&_sk=999529996&view=c&wchp=dGLbVlb-zSkzk&_acct=C000005238&_version=1&_userid=109269&md5=4f61ff0d3f18687734a9830f3999c2ad&ie=f.pdf

message system, which supports the asynchronous exchange of textual messages between groups of users. The conceptual underpinning of groupware - the merging of computer and communications technology - applies to a broad range of systems. Information sharing in the groupware context leads to unexplored problems in distributed systems and user interface design that emphasizes group interaction.


CONTACT INFORMATIONDepartment of Management Information SystemsEller College of Business and Public AdministrationThe University of ArizonaTucson, Arizona 85721(520) 621-4475 Telephone(520) 621-2433 [email protected]://www.cmi.arizona.edu/home/Jay%20Nunamaker.html

EDUCATIONPh.D. – Operations Research & Systems, Case Western Reserve University, 1969MS – Industrial & Systems Engineering, University of Pittsburgh, 1965BS – Industrial Management, Carnegie Mellon University, 1964BS – Mechanical Engineering, University of Pittsburgh, 1960

RESEARCH INTERESTSComputer supported collaboration and decision support to improve productivity and communication.

KEY PUBLICATIONS Nunamaker, J.F., Jr.; Dennis, A.R.; Valacich, J.S.; Vogel, D.R.; and George, J.F. Electronic

meeting systems to support group work. Communications of the ACM, 34, 7 (July 1991), 40-61.


Jay F. Nunamaker, Jr.– Regents' & Soldwedel Professor of MIS, Computer

Science and Communication– Director, Center for the Management of Information Department of Management Information SystemsThe University of Arizona (Tucson, Arizona)

COLLABORATION

http://www.cmi.arizona.edu/home/Jay%20Nunamaker.html


Nunamaker, J.F., Jr; Chen, M.; and Purdin, T.D.M. Systems development in information systems research. Journal of Management Information Systems, 7, 3 (Winter 1990-91), 89-106.

Nunamaker, J.F., Jr.; Briggs, R.O.; Mittleman, D.D.; Vogel, D.R.; and Balthazard, P.A. Lessons from a dozen years of group support systems research: a discussion of lab and field findings. Journal of Management Information Systems, 13, 3 (Winter 1996-97), 163-207.

TRIVIA / MISCELLANEOUSJay attended the University of Pittsburgh on a wrestling scholarship.


CONTACT INFORMATION

The Fuqua School of BusinessDuke UniversityBox 90120Durham, NC 27708-0120

(919) 660-7848 Telephone(919) 681-6245 Facsimile

[email protected]://www.fuqua.duke.edu/faculty/alpha/gd.htm

EDUCATIONPhD - Business Administration, Texas Tech University, 1982MA - Psychology, Fairleigh Dickinson University, 1977BA - Psychology, Villanova University, 1975

RESEARCH INTERESTS

Electronic Communication, Organization Design, Distributed Teams, and Online Communities

KEY PUBLICATIONS

DeSanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management Science, 33(5), 589-609.

DeSanctis, G., Poole, M. S., Lewis, H., & Desharnais, G. (1992). Using computing in quality team meetings: Some initial observations from the IRS-Minnesota project. Journal of Management Information Systems, 8(3), 7-26.

DeSanctis, G., Sambamurthy, V., & Watson, R. T. (1988). A software environment for GDSS Research. In E. S. Weber (Ed.), DSS-88 Transactions: Eighth International Conference on Decision Support Systems (pp.3-12). Providence, RI: The Institute of Management Sciences.

Toward friendly user MIS implementation. Communications of the ACM, 26(10), 1983, 732-738.

Using GDSS to facilitate group consensus: Some intended and unintended consequences, MIS Quarterly 12(3), 1988, 463-478.


Gerardine DeSanctis- Thomas F. Keller Professor of Business AdministrationThe Fuqua School of BusinessDuke University (Durham, NC)

COLLABORATION

http://www.fuqua.duke.edu/faculty/alpha/gd.htm


http://www.stanford.edu/

TRIVIA / MISCELLANEOUSGerardine is a primary developer of SAMM: Software Aided Meeting Management (with Poole & Dickson).


CONTACT INFORMATIONDepartment of Management Information SystemsSchool of BusinessQueens UniversityOntario, Canada(613) 533-2361 ext. 32361 Telephone(613) 533-2013 [email protected]://business.queensu.ca/research/faculty/files/cv961329023.pdf

EDUCATIONPh.D. – Business Administration, University of Minnesota, 1985MBA – York University, 1977BS – Honors Bachelor of Mathematics, University of Waterloo, 1974

RESEARCH INTERESTSElectronic brainstorming in management information systems

KEY PUBLICATIONS DeSanctis, G.; Dennis, A.R.; Gallupe, R.B.; A foundation for the study of group decision

support systems. Management Science, 36 (1990), 689-703. Gallupe, R.B.; Dennis, A.R.; and Cooper, W.H. Electronic Brainstorming and Group Size.

Academy of Management Journal, 35, 2 (1992), 350-369.TRIVIA / MISCELLANEOUSFellow of Life Management Institute (FLMI).


R. Brent Gallupe– Associate Dean of Faculty Development Professor– Director, Queens Centre for Knowledge-based

Enterprises Department of Management Information SystemsQueens University (Ontario, Canada)

COLLABORATION

http://business.queensu.ca/research/faculty/files/cv961329023.pdf


CONTACT INFORMATIONDepartment of ManagementMcCombs School of BusinessThe University of Texas at AustinAustin, Texas 78712(512) 471-9609 Telephone

[email protected]://www.mccombs.utexas.edu/dept/management/faculty/profiles/index.asp?addTarget=41

EDUCATIONPh.D. – Purdue UniversityBSME – University of MissouriMSIE – University of Missouri

RESEARCH INTERESTSOrganizational change, organizational design, and organizational decision making.

KEY PUBLICATIONS Huber, G.P. Issues in the design of group decision support systems. Management

Information Systems Quarterly, Sept. 1984, 195-204. Huber, G.P. Cognitive style as a basis for MIS and DSS designs: much ado about nothing?

Management Science, 29 (5), 1983, 567-579. Huber, G.P. Organizational information systems: determinants of their performance and

behavior. Management Science, 28 (2), 1982, 138-155.

TRIVIA / MISCELLANEOUSMember of the Academy of Management Journals Hall of Fame.


George P. Huber– Charles and Elizabeth Prothro Regents Chair in

Business AdministrationDepartment of ManagementThe University of Texas at Austin (Austin, Texas)

COLLABORATION

http://www.mccombs.utexas.edu/dept/management/faculty/profiles/index.asp?addTarget=41

http://www.mccombs.utexas.edu/dept/management/faculty/profiles/index.asp?addTarget=41


CONTACT INFORMATIONInformation Systems DepartmentNew Jersey Institute of TechnologyUniversity HeightsNewark, NJ 07102

(973) 596-3366 Telephone(973) 596-5777 [email protected] http://eies.njit.edu/~turoff/

EDUCATIONPh.D. – Physics, Brandeis University, 1965B. A. – Mathematics and Physics, University of California at Berkeley, 1958

RESEARCH INTERESTSInformation Systems, Computer Mediated Communication Systems, Delphi Design, Policy Analysis, Planning Methodologies, Interface Design, Systems Evaluation, Technological Forecasting & Assessment, Collaborative Systems & Group DSSs, Office Automation, Management Information Systems, Social Impacts of Computer & Information Systems, and Management of Computer and Information Systems

KEY PUBLICATIONS Turoff, Murray, Delphi and its Potential Impact on Information Systems, AFIPS Conference

Proceedings, Volume 39. (1971), 317-326


Dr. Murray Turoff– Distinguished Professor– Hurlburt Professor of MISInformation Systems DepartmentNew Jersey Institute of Technology (Newark, NJ)

COLLABORATION

http://eies.njit.edu/~turoff/


Turoff, Murray, Computer Mediated Communication Requirements for Group Support, Journal of Organizational Computing, Volume 1, Number 1. (1990), 85-113

TRIVIA / MISCELLANEOUSOriginated the Policy Delphi technique, an extension of the Delphi Method for use in Policy & Decision Analysis. First to automate & conduct Delphi's in real time on time-sharing computer systems in 1970. Designed the nationwide Management Information System that was used for the 1971 Wage-Price-Rent Freeze (EMISARI). Introduced the concept of Computerized Conferencing and am responsible for the implementation, utilization and early evaluation work on this unique combination of an information and communication system. Developed and implemented the first Computer Mediated Communications System (CMC) tailored to facilitate group communications (Delphi Conference in 1970).


CONTACT INFORMATIONSchool of Accounting, Information Systems & Business LawCollege of Business and EconomicsWashington State UniversityPullman, Washington 99164(509) 335-1112 Telephone

[email protected]://www.cbe.wsu.edu/~jsv

EDUCATIONPh.D. – Management Information Systems, University of Arizona, 1989MBA – General Management, University of Montana, Missoula, 1983BS – Computer Science, University of Montana, Missoula, 1982

RESEARCH INTERESTSElectronic commerce, the diffusion of technology in organizations, group decision behavior, and distance learning.

KEY PUBLICATIONS Nunamaker, J.F., Jr.; Dennis, A.R.; Valacich, J.S.; Vogel, D.R.; and George, J.F. Electronic

meeting systems to support group work. Communications of the ACM, 34, 7 (July 1991), 40-61.

Nunamaker, J.F., Jr.; Dennis, A.R.; Valacich, J.S. Information Technology for Negotiating Groups: Generating Options for Mutual Gain. Management Science, 37, 10 (Oct 1991), 1325-1346.

Connolly, T.; Jessup, L.M.; Valacich, J.S. Effects of anonymity and evaluative tone on idea generation in computer-mediated groups. Management Science, 36 (1990), 689-703.

TRIVIA / MISCELLANEOUSJay Nunamaker was his Ph.D. advisor.


Joseph S. Valacich– The Marian E. Smith Presidential Endowed Chair–The George and Carolyn Hubman Distinguished

Professor in Information SystemsSchool of Accounting, Information Systems &

Business LawWashington State University (Pullman, Washington)

COLLABORATION

http://www.cbe.wsu.edu/~jsv


Database

1960Magnetic disks

replace magnetictape drives

1960'sFoundation of

relational databases

1960's and 1970'sHierarchical, network, and relational technologies develop

1961IDS created

1973Ingres developed

1976Honeywell launches

first commercialproduct based

on relational technology

1980Oracle creates

first database builton SQL

Late 80'sObject oriented

technologies emerge

1990'sObject-Relational

technology is the trend

1995First object oriented dbms

1970Network databasemodel continues

1970E.F. Codd's

article onrelational databases

As the core of MIS field, database has been a distinct research area since the 1960s. People

started to find efficient and economic solutions to store and manage data. The term

"database" emerged to capture the sense that the information stored within a computer

could be conceptualized, structured, and manipulated independently of the specific machine

on which it resided. Nowadays, database is a industry about $8 billion in annual revenue.

Some well-known players in this field include: IBM Corporation, Oracle Corporation, Informix

Corporation, Sybase Incorporated, and Microsoft Corporation.

The “Pre-historic” database research can be traced back to early 1960s. In 1961, Charles

Bachman at General Electric Company introduced the integrated data store (IDS) system, a

pioneering database management system that took advantage of the new storage

technology and included novel schemas and logging, among other features.


DATABASE

From the 1960s to the 1970s, various database technologies including Hierarchical and

Network systems are developed. The revolutionary development in database field started in

the 1970s when relational technologies emerged. Edgar F.(Ted) Codd was then a researcher

at IBM San Jose research lab. Trained as a mathematician in Oxford, Codd developed

relational model based on set theory and predicate logic. In his landmark paper in 1970, “A

Relational Model of Data for Large Shared Data Banks”, Codd pointed out a new way to

organize and access data. As successful as it is today, relational model then was not

embraced and adopted. People saw it no more than intellectual curiosity and IBM was not

willing to switch gears from their highly invested database product IMS to something they

were not familiar with.

Thanks to Codd’s zeal in promoting his research, two groups started to develop database

models based on Codd’s relational theory. One is within IBM where they developed System R

that is the predecessor of DB2 and SQL; the other one is Ingres began at University of

California at Berkeley with military and national Science Foundation funding by Michael

Stonebraker and Eugene Wong.

Along with research and development in relational database management technology,

database design became a discipline in the field. In 1976, Peter Pin-Shan Chen published

“The Entity-Relationship Model--Toward a Unified View of Data”. A data model, called the

entity-relationship model, is proposed. This model incorporates some of the important

semantic information about the real world, and can be used as a basis for unification of

different views of data. The entity-relationship model is now widely used in conceptual

database design and extended by many researchers in multiple fields.

Object-oriented is another database technology. It appears as a concept at early 80’s. It

bases on the concept of objects, which are a collection of data items and the operations that


DATABASE

can be executed on them. This research was done to overcome many restrictions imposed

by the relational model on certain types of data.

The Object Relational Model is a relatively recent development and is likely to have a large

impact. It is not a new technology but a combination of the relational and the object

oriented models to overcome some obvious drawbacks of relational model —one of them is

that the relational model was not designed for, and is not able to cope effectively with, the

new types of data it is expected to store. For example, multimedia (audio, video, and image)

databases are something cannot be realized with relational technologies alone. This model

was also triggered by the increasing use of object oriented programming languages and the

realization that there is a large degree of impedance mismatch between these and the

DBMS software. This will also be the direction of database model research in the near future.

For instance, Michael Stonebraker direct Postgres at UC Berkeley is in this direction.

There are some other subfields that have attracted a lot of research interests worth

mentioning here: Distributed database, Database integration, Temporal and Spatial

databases, Scientific databases especially in Bioinformatics and Chemoinformatics.

DATABASE KEY PUBLICATIONS:

The Entity-Relationship Model – Toward a Unified View of DataChen, P. P.ACM Trans. Database Systems, 1(1) 1976, 9-36. [Link]

Model Classification Quadrant: Relevant, Technical, Theory.

A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world, and can be used as a basis for unification of different views of data. The entity-relationship model is now widely used in conceptual database design.

A Relational Model of Data for Large Shared Data BanksCodd, E. F.Communications of the ACM, 12(6), 1970, 377-387. [Link]



DATABASE

http://portal.acm.org/citation.cfm?id=362685

http://citeseer.nj.nec.com/519283.html

A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced in this paper. Moreover, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model. Codd’s model laid the theoretical foundation for relational databases.


Allocating Data and Operations to Nodes in Distributed Database Design. March, S. T., & Rho, S.IEEE Transactions in Knowledge and Data Engineering, 72, 1995, 305-317. [Link]


A comprehensive mathematical modeling approach to allocating data and operations to nodes in a computer network is proposed. The approach first generates units of data to be allocated from a logical data model representation. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach.

Querying Object-Oriented DatabasesMichael Kifer, Won Kim, Yehoshua SagivSIGMOD Conference 1992: 393-402. [Link]

Model Classification Quadrant: Relevant, Technical, Application.

The author presents a language for querying object-oriented databases. The language is built around the idea of extended path expressions and first-order formalization. The language incorporates features not found in earlier proposals; it is easier to use and has greater expressive power.

The Theory of Joins in Relational DatabasesA.V. Aho, C. Br, AND J. D. UllmanProceedings, 18th IEEE Symp. on Foundations of Computer Science, Providence, R.I., Oct. 1977. [Link]

Model Classification Quadrant: Relevant, Technical, Theory.

This paper presents efficient algorithms to determine whether the join of several relations has the intuitively expected value (is lossless) and to determine whether a set of relations has a subset with a lossy join. These algorithms assume that all data dependencies are functional.

Semantic Model Support for Geographic Information Systems. Sudha Ram, Jinsoo Park & George BallIEEE Computer, Vol. 32, No. 5, May 1999, 74-81. [Link]


This paper presents a semantic model called the Unifying Semantic Model (USM) and a software tool using it. The USM can be used for modeling the complex interrelationships and semantics found in manufacturing environment. It is based on enhancements to existing semantic models. It can provide a means of specifying the Universe of Discourse for any interchange of information.


DATABASE

http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=16523&page=1

http://portal.acm.org/citation.cfm?id=320091&jmp=indexterms&dl=GUIDE&dl=ACM

http://citeseer.nj.nec.com/kifer92querying.html

http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=382299

Context Interchange: Overcoming the Challenges of Large-Scale Interoperable Database Systems in a Dynamic EnvironmentCheng Hian Goh, Stuart E. Madnick, Michael SiegelCIKM 1994: 337-346. [Link]


This paper highlights the problem of receiver heterogeneity, scalability, and evolution which have received little attention in the literature, provides an overview of the Context Interchange approach to interoperability, illustrates why this is able to better circumvent the problems identified, and forges the connections to other works by suggesting how the context interchange framework differs from other integration approaches in the literature.

Access Path Selection in a Relational Database Management SystemP. Selinger, M. Astrahan, D. Chamberlin, R. Lorie, T. PriceSIGMOD (1979). [Link]


This paper describes how System R (experimental DBMS developed for the research purpose by IBM) chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a Boolean expression of predicates. This is a research for data manipulation and access procedure.

SEQUEL: A Structured English Query Language. D. D. Chamberlin and R. F. BoyceSIGMOD Workshop, Vol. 1 1974: 249-264. [Link]


In this paper, they introduced the data manipulation facility for a structured English query language (SEQUEL) which can be used for accessing data in an integrated relational database and which is so popular and widely used in current database management industry.

System R: Relational Approach to Database Management. M. Astrahan, et al.TODS 1(2): 97-137 (1976). [Link]


This paper contains a description of the overall architecture and design of the System R that is a DBMS which provides a high level relational data interface. System R introduced the SQL language and also demonstrated that a relational system could provide good transaction processing performance.


DATABASE

http://portal.acm.org/citation.cfm?id=320455.320457&dl=GUIDE&dl=ACM&idx=J777&part=periodical&WantType=periodical&title=ACM%20Transactions%20on%20Database%20Systems%20(TODS)&CFID=11111111&CFTOKEN=2222222

http://www.cs.uml.edu/~cchen/574-F03/reading/cb74.pdf

http://www.cs.berkeley.edu/~brewer/cs262/AccessPath.pdf

http://www.informatik.uni-trier.de/~ley/db/conf/vldb/Madnick95.html

A Methodology for Creating User Views in Database DesignVeda C. Storey, Robert C. GoldsteinACM Transaction on Database Systems. 13(3): 305-338 (1988). [Link]


This paper describes the knowledge base of the View Creation System (VCS; an expert system that covers from the information requirements gathering phase to the E-R model and its logical design phase). Key aspects of the methodology are illustrated by applying VCS’ knowledge base to an actual database design task.

Understanding Semantic Relationships. Veda C. StoreyVLDB Journal, Vol.2,pp.455-487, (1993). [Link]


This article explores some of the lesser-recognized semantic relationships and discusses both how they could be captured, either manually or by using an automated tool, and their impact on database design.

Temporal Data ManagementRichard T. Snodgrass, Christian S. JensenIEEE Transaction on Knowledge & Data Engineering, 11(1): 36-44 (1999). [Link]


This paper introduces the reader to temporal data management, surveys state-of-the-art solutions to challenging aspects of temporal data management, and points to research directions.

Model Independent Assertions for Integration of Heterogeneous SchemasStefano Spaccapietra, Christine Parent, Yann DupontVLDB Journal 1(1): 81-126 (1992). [Link]


This paper investigates the assertion-based approach, in which the DBA’s action in integrating local schemas is limited to pointing out corresponding elements in the schema and to defining the nature of the correspondence in between rather than just using a view definition language. He tries to resolve and improve the heterogeneous database integration modeling issue.


DATABASE

http://www.acm.org/sigmod/vldb/journal/VLDBJ1/P081.pdf

http://csdl.computer.org/comp/trans/tk/1999/01/k0036abs.htm

http://www.informatik.uni-trier.de/~ley/db/journals/vldb/Storey93.html

http://portal.acm.org/citation.cfm?id=45064&jmp=citings&dl=GUIDE&dl=ACM

CONTACT INFORMATIONLouisiana State UniversityDepartment of Computer Science298 Coates Hall, Tower RoadBaton Rouge, LA 70803(225) 578-1495 Telephone(225) 578-1465 [email protected] http://bit.csc.lsu.edu/~chen/

EDUCATIONPh.D. – Computer Science / Applied Mathematics, Harvard University, 1973MS – Computer Science / Applied Mathematics, Harvard University, 1970BS – Electrical Engineering, National Taiwan University, 1968

RESEARCH INTERESTSDatabase design, Entity-Relationship Model, Software Engineering in particular Computer-Aided Software Engineering

KEY PUBLICATIONS Chen, P. The Entity-Relationship Model--Toward a Unified View of Data. ACM Transactions

on Database Systems, Vol. 1, No. 1, March 1976, Pages 9 – 36 Chen, P. The Entity-Relationship Model-- A basis for the Enterprise View of Data. Proc. of

National Computer Conference, 1977, AFIPS Press, Pages 77-84

TRIVIA / MISCELLANEOUS


Peter Pin-Shan Chen– Murphy J. Foster Professor of Computer Science Department of Computer ScienceLouisiana State University (Baton Rouge, Louisiana)

DATABASE



Peter is a fellow of ACM, IEEE and AAAS and has been awarded the IEEE Harry Goode Award as well as the ACM/AAAI Allen Newell Award. His original paper in the Entity-Relationship Model is one of the 38 most influential papers in Computer Science and 35th most cited article in Computer Science.


EDUCATIONPh.D. – Computer Science, University of Michigan at Ann Arbor, 1965BS – Mathematics / Chemistry, Oxford University, England

RESEARCH INTERESTSRelational database technologies

KEY PUBLICATIONS Codd, E. F. A Relational Model of Data for Large Shared Data Bank. Communication of

ACM 13(6): 377-387. 1970

TRIVIA / MISCELLANEOUSEdgar served as pilot in Royal Air Force in World War II.

One of the normalized forms in Database normalization – the Boyce-Codd Normal Form – is named after him.

Edgar received a Turning Award in 1981.

His research on Relational Databases was not embraced by IBM. Later IBM was beaten to the market by a Silicon Valley entrepreneur who used ideas from Codd’s paper.


Edgar F. Codd (1924-2003)– Researcher– Founder of relational databases IBM Research Laboratory (San Jose, CA)

DATABASE

http://www.computerworld.com/computerworld/records/images/story/16KCsofar_codd_secondary.jpg

CONTACT INFORMATION77 Massachusetts Ave.Building E53-321Cambridge, MA 02139-4307 [email protected] http://web.mit.edu/smadnick/www/home.html

EDUCATIONPh.D. – Computer Science, Massachusetts Institution of Technology, 1972MS – Management, Massachusetts Institution of TechnologyBS – Electrical Engineering, Massachusetts Institution of Technology

RESEARCH INTERESTSConnectivity among disparate distributed information systems, database technology, software project management, and the strategic use of information technology

KEY PUBLICATIONS Software Project Dynamics: An Integrated Approach, Co-authored by T.K. Abdel-Hamid

Prentice-Hall 1991 Allen Moulton, Stuart E. Madnick, Michael Siegel: Semantic Interoperability in the Fixed

Income Securities Industry: A Knowledge Representation Architecture for Dynamic Integration of Web-Based Information. HICSS 2003: 287

TRIVIA / MISCELLANEOUSDr. Madnick is a prolific writer and is the author or co-author of over 250 books, articles, or reports including the classic textbook, Operating Systems (McGraw-Hill), and the book, The Dynamics of Software Development (Prentice-Hall).

He has served as the head of MIT's Information Technologies Group for more than ten years. During that time the group has been consistently rated #1 in the nation among business school information technology programs.


Stuart E. Madnick– John Norris Maguire Professor of Information

Technology– Leaders for Manufacturing Professor of

Management Science Sloan School of Management Massachusetts Institute of Technology (Cambridge, MA)

DATABASE



CONTACT INFORMATIONOwen Graduate School of Management Vanderbilt UniversityNashville, TN 37203(615) 322-7043 Telephone(615) 343-7177 [email protected]://mba.vanderbilt.edu/Sal.March/

EDUCATIONPh.D. – Operations Research / Information Processing, Cornell University, 1978M.S. – Operations Research, Cornell University, 1975B. S. – Industrial Engineering / Operations Research, 1972

RESEARCH INTERESTSDatabase development, design and integration. Data modeling.

KEY PUBLICATIONS March, S. T. and Severance, D. G. "The Determination of Efficient Record Segmentations

and Blocking Factors for Large Shared Databases," ACM Transactions on Database Systems, Vol. 2, No. 3, September 1977.


Salvatore T. March– David K. Wilson Professor of Management

(Information Technology)Owen Graduate School of ManagementVanderbilt University (Nashville, Tennessee)

DATABASE

http://mba.vanderbilt.edu/Sal.March/


http://www.state.tn.us/

March, S. T., Severance, D. G., and Wilens, M. "Frame Memory: A Storage Architecture to Support Rapid Design and Implementation of Efficient Databases," ACM Transactions on Database Systems, Vol. 6, No. 3, September 1981.

TRIVIA / MISCELLANEOUSAssociate Editor for MIS Quarterly 1993 to 1998.

Associate Editor for Journal of Database Management since 1995.


CONTACT INFORMATIONDepartment of Management Information SystemsCollege of Business and Public AdministrationThe University of ArizonaTucson, Arizona 85721(520) 621-2748 Telephone(520) 621-4113 [email protected] http://vishnu.bpa.arizona.edu/ram/index.html

EDUCATIONPh.D. – MIS / Corporate Finance, University of Illinois at Urbana Champaign, 1985.MBA – MIS, Indian Institute of Management, Calcutta, India, 1981 BS – Chemistry / Mathematics / Physics, University of Madras, India, 1979

RESEARCH INTERESTSE-business infrastructure and strategy, Automate design tools for database design, distributed and heterogeneous database systems, intelligent agents and digital libraries, design of distributed knowledge based systems

KEY PUBLICATIONS Ram, S., Curran S. M, “An Automated Tool for Relational Database Design, Information

Systems”, Vol. 14, No. 3, 1989, pp. 247-259 Ram, S., "Heterogeneous Distributed Database Systems", IEEE Computer, Vol. 24, No.

12, Dec. 1991, pp. 7-12.


Sudha Ram– Eller Professor of Management Information SystemsDepartment of Management Information SystemsCollege of Business and Public AdministrationUniversity of Arizona (Tucson, Arizona)

DATABASE



Ram, S., "Deriving Functional Dependencies from the Entity Relationship Model", Communications of the ACM, Vol. 38, No. 9, Sept 1995 pp. 95-107

TRIVIA / MISCELLANEOUSAssociate editor for multiple renowned journals, including: Information Systems Research, Journal of Database Management, Journal of Information Technology Management, Decision Support Systems Journal, etc.

CONTACT INFORMATIONDepartment of Computer Science711 Gould SimpsonUniversity of ArizonaP.O. Box 210077Tucson, AZ 85721-0077(520) 621-6370 Telephone(520) 621-4246 [email protected]://www.cs.arizona.edu/people/rts/

EDUCATIONPh.D. – Computer Science, Carnegie Mellon UniversityMS – Computer Science, Carnegie Mellon UniversityBS – Physics, Carleton College

RESEARCH INTERESTSTemporal databases, query language design, query optimization and evaluation, storage structures and database design.


Rick Snodgrass– Professor of Computer Science– ACM fellowDepartment of Computer ScienceUniversity of Arizona (Tucson, Arizona)

DATABASE



KEY PUBLICATIONS Giedrius Slivinskas, Christian S. Jensen, and Richard T. Snodgrass, "A Foundation for

Conventional and Temporal Query Optimization Addressing Duplicates and Ordering," IEEE Transactions on Knowledge and Data Engineering, 13(1):21--49, January/February 2001

Richard T. Snodgrass, Developing Time-Oriented Database Applications in SQL, Morgan Kaufmann Publishers, Inc., San Francisco, July, 1999, 504+xxiii pages

TRIVIA / MISCELLANEOUSRick is Editor-in-Chief of the ACM Transactions on Database Systems.

Associate Editor of the International Journal on Very Large Databases,

Served on the editorial board of the IEEE Transactions on Knowledge and Data Engineering

Winner of ACM/AAAI Allen Newell Award


CONTACT INFORMATIONDepartment of Electrical Engineering and Computer Science621 Soda HallBerkeley, CA 94720-1770(510) 642-5799 [email protected] http://epoch.cs.berkeley.edu:8000/nasa_e2e/mike.html

EDUCATIONPh.D. – Information and control engineering, University of Michigan, 1971MS – University of Michigan BS – Princeton University

RESEARCH INTERESTSOperating systems and expert systems, DBMS support for visualization environments and next-generation distributed DBMSs

KEY PUBLICATIONS Stonebraker, M.,"The Design of the POSTGRES Storage System," Proc. 1987 VLDB

Conference, Brighton, England, September 1987 Stonebraker, M. The design and implementation of INGRES. ACM, 1, 3, (1976), 189-122.

TRIVIA / MISCELLANEOUSDr. Stonebraker founded Ingres Corp. in 1980, Ingres later was purchased by Computer Associates.


Michael Stonebraker– Professor of Electrical Engineering and Computer

Sciences Department of Electrical Engineering and Computer

ScienceUniversity of California at Berkeley (Berkeley,

California)

DATABASE



INGRES, the company's primary product, was a commercialization of Dr. Stonebraker's INGRES research project into relational database management systems (RDBMS) at Berkeley


CONTACT INFORMATIONDepartment of Computer Information SystemsJ. Mack Robinson College of Business AdministrationGeorgia State UniversityP.O. Box 4015Atlanta, Georgia 30302-4015(404)-651-3894 [email protected] http://www.cis.gsu.edu/~vstorey/

EDUCATIONPh.D. – Management Information Systems, University of British Columbia, 1986MBA – Queen’s University, 1980BS – Mathematics / Computer Science, Mt. Allison University, 1978

RESEARCH INTERESTSDatabase management systems, intelligent systems, and ontology development.

KEY PUBLICATIONS Sugumaran, V. and Storey, V.C., “Ontologies for Conceptual Modeling: Their Creation,

Use and Management,” Data and Knowledge Engineering, Vol.42, No.3, November 2002. pp.251-271

Storey, V.C., and Dey, D., “A Methodology for Learning Across Application Domains for Database Design Systems,” IEEE Transactions on Knowledge and Data Engineering, Vol.14, No.1, January/February 2002, pp.13-28


Veda Storey– Professor of Computer Information Systems– Professor of Computer Science College of Business AdministrationGeorgia State University (Atlanta, Georgia)

DATABASE



CONTACT INFORMATIONCyber Database SolutionsAustin, Texas

(512) 771-9376 Telephone(512) 329-0244 [email protected] http://www.cyberdb.com/index.html

EDUCATIONPh.D. – University of Illinois in Urbana-Champaign, 1980M.S./ B. S. – Massachusetts Institute of Technology

RESEARCH INTERESTSRelational, Object-Oriented, & Object-relational database systems, Data Warehousing, Business intelligent systems (OLAP, Data Mining), Internet software infrastructure technology (HTML/XML, e-Commerce systems, etc.)

KEY PUBLICATIONS Won, K. Integrating an object-oriented programming system with a database system.

ACM Conference Proceedings, (1988), 142-152. Kifer, M.; Won, K.; and Sagiv, Y. Querying object-oriented databases. Proceedings of the

ACM SIGMOD, (1992).



Kim Won– CEOCyber Database Solutions (Austin, Texas)

DATABASE

http://www.cyberdb.com/index.html


Dr. Won Kim served 12 years as the Editor-in-Chief and Associate Editor of ACM Transactions on Database Systems

Chairman of ACM SIGKDD (special interest group on knowledge discovery and data mining)

Dr. Won Kim is the CEO and founder of Cyber Database Solutions, Inc.


Decision Science

1980Framework

for DSS

1987Foundationof GDSS

1996Emergence ofE-Commerce

1997Focus Shifts

from OMto SCM

2000Proof of 2-Player

Zero-Sum StochasticGame Equilibrium

2001Convergence of

E-Commerceand SCM

Decision Science (DS) is a broad MIS research classification that encompasses three major

subcategories ranging from the fairly old (OR/MS – Operations Research/Management

Science) to the more recent (DSS – Decision Support Systems) to the very new (EC/SCM – E-

Commerce/Supply Chain Management). Although the first OR/MS papers began appearing in

the 1940’s, the vast majority of publications in the other subcategories, particularly EC/SCM,

have occurred over the past decade (see timeline). Because most of the recent productivity

and efficiency improvements in the area of DS have come about through research in the

newer subcategories, DSS and EC/SCM are likely to continue being expanding research

areas in the future, while OR/MS should continue contributing to MIS research at a steadier

pace.

The very broad nature of DS makes it a research domain that is difficult to define and

categorize. In fact, the wide variety of research performed across the three subcategories of

DS has often caused them to be listed as their own separate high level research domains in

other models of MIS. However, our group felt that this should not be the case for our model,

because while we acknowledge the differences in these subcategories, we believe that the

research performed in each is similar in one key way. Specifically, regardless of

subcategory, all DS research done within an MIS context attempts ‘to apply statistical,

economic, psychological, and/or technological tools and techniques to assist in decision


DECISION SCIENCE

making’. Because we chose this as the working definition for DS within our model and also

in light of recent trends, our model of MIS heavily emphasizes DSS (5 papers, 2 researchers)

and EC/SCM (6 papers and 3 researchers between them), while giving a slightly lesser focus

to OR/MS (2 papers, 1 researcher).

Within the context of our model, the key decision science papers include:

DECISION SUPPORT SYSTEMS

DSS Design: A Systemic View of Decision SupportAriav, G; Ginzberg, M.Communications of the ACM, 28(10), 1985, 1045-1052. [Link]


This paper suggests a systemic view of DSS. The author illustrates the premise of the systemic view by considering the following five aspects simultaneously: environment, role, components, arrangement of components, and the resources required to support the system. Though the topic of this article is DSS, the purpose of this article is to present a comprehensive view of DSS using a systemic framework as an organizing concept. The concepts of systems theory integrate the disparate perspectives in the DSS literature into a consistent and coherent body of knowledge.

A Framework for the Development of Decision Support SystemsSprague, R. MIS Quarterly, 4(4), 1980, 1-26. [Link]


This paper illustrates a framework for the development of DSS by examining and reconciling several alternative views of DSS. The framework articulates and integrates major concerns of several stakeholders in the development of DSS: the executives and professionals who use them, the information specialists who build them, the system designers who create and assemble the technology on which they are based, and the researchers who study the DSS domain.

An Experimental Investigation of the Impact of Computer-Based Decision Aids on Decision-Making StrategiesTodd, P.; Benbasat, I.Information Systems Research, 2(2), 1991, 87-115.


This paper proposes the use of a cognitive effort model of decision making to explain decision-maker behavior when assisted by DSS. The central proposition of the article is that specific features can be incorporated within a DSS that will alter the effort required to implement a particular strategy, and thus influence strategy selection by the decision-maker. Based on three empirical experiments, the author contends that decision makers tend to adapt their strategy selection to the type of decision aids available in such a way as to reduce cognitive effort.


DECISION SCIENCE

http://links.jstor.org/sici?sici=02767783(198012)4:4%3C1:AFFTDO%3E2.0.CO;2-Z

http://portal.acm.org/citation.cfm?id=4372.4374&dl=GUIDE&dl=ACM&CFID=14483353&CFTOKEN=15342334


The Design and Use of Laboratory Experiments for DSS EvaluationGardner, C.; Marsden, J.; Pingry, D.Decision Support Systems, 9(4), 1993, 369-379. [Link]


This paper focuses on an ex-ante evaluation whose emphasis is on predicting the likely impact of DSS alternatives on a given task set. The authors have outlined a methodology for developing such information to avoid costly DSS selection errors using the induced-value methodology of experimental economics. An example experiment is detailed and initial results are presented relating to one general DSS hypothesis and one implication derived from a specific theory of DSS portfolio selection.

ELECTRONIC COMMERCE / SUPPLY CHAIN MANAGEMENT

Electronic Commerce: Structures and IssuesZwass, V.International Journal of Electronic Commerce, 1(1), 1996, 3-23. [Link]


This paper presents a hierarchical framework of E-commerce development, as well as of analysis, range from the wide-area telecommunications infrastructure to electronic marketplaces and electronic hierarchies enabled by E-commerce. Several nodal problems were discussed that defined future development in E-commerce, including integrating electronic payment into the buying process, building a consumer marketplace, the governance of electronic business, and the new intermediation.

Toward a Unified View of Electronic CommerceRiggins, F.; Rhee, H. Communications of the ACM, 41(10), 1998, 88-96. [Link]


This paper presents different systematic views of e-commerce and points out two different dimensions that characterize Internet technology applications: 1) location of application user relative to system firewall, and 2) type of relationship affected.

Frictionless Commerce? A Comparison of Internet and Conventional RetailersBrynjolfsson, E.; Smith, M.Management Science, 46(4), 2000, 563-585. [Link]


This paper examines the hypothesis that electronically mediated markets would have less friction than comparable conventional markets, through a empirical price comparison between the two types of markets. The conclusion is that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers. The authors conclude by questioning whether these differences are a symptom of an immature market or reflect more permanent characteristics of Internet retailing.


DECISION SCIENCE

http://pubsonline.informs.org/main/pdfstore/0bf2ea554c_article.pdf



http://www.gvsu.edu/ssb/ijec/v1n1/p003full.html

http://portal.acm.org/citation.cfm?id=161459&jmp=citings&coll=GUIDE&dl=GUIDE

E-Fulfillment: Winning the Last Mile of E-CommerceLee, H.; Whang, S.Sloan Management Review, 42(4), 2001, 75-90. [Link]


This paper provides an overview of how Internet technologies have allowed existing supply chain strategies to be applied in new and innovative ways to fulfill orders more quickly and efficiently. In their analysis, the authors explain two core concepts for E-enabled supply chains, as well as five of the more prominent supply chain strategies they underlie. The paper concludes by pointing out ways that companies can extend E-supply chain management and E-fulfillment beyond mere cost containment.

Supply Chain Inventory Management and the Value of Shared InformationCachon, G.P.; Fisher, M.Management Science, 46(8), August 2000, 1032-1048. [Link]


This paper builds a simple supply chain model and runs simulations to compare the performance of the model with and without the full information sharing made possible by information technology (IT). Upon comparing these metrics, the authors then take their analysis a step further by comparing the information sharing-related performance improvements with other IT-related performance improvements. In the end, they conclude that it is more valuable to use IT to help speed the physical flow of goods through a supply chain rather than to expand the flows of information in a supply chain.

Information Distortion in a Supply Chain: the Bullwhip EffectLee, H.; Padmanabhan, V.; Whang, S.Management Science, 43, 1997, 546-558. [Link]


This paper discusses how wide variations in orders result in information distortion effects that are compounded as they move downward through a supply chain. The authors have named this phenomenon the bullwhip effect, and claim that it is caused by four major factors that they describe in detail. In addition, they conclude with four suggestions of how companies can counteract and control the bullwhip effect in their supply chains.


DECISION SCIENCE

http://teaching.ust.hk/~ieem341/papers/Lee97.pdf

http://opim.wharton.upenn.edu/~cachon/pdf/cachon_fisher_ms.pdf

http://smr.mit.edu/past/2001/smr4245.html

OPERATIONS RESEARCH / MANAGEMENT SCIENCE

Organizational Architecture and Success in the Information Technology IndustryMendelson, H.Management Science, 46(4), 2000, 513-529. [Link]


This paper studies an organizational architecture called information-age (IA) architecture. The organizational IQ is measured by aggregating five principles: information awareness, decision architecture, knowledge transparency, activity focus, and IA network. An empirical study result shows that higher organizational IQ is associated with higher profitability, growth, and business success.

Two-Player Stochastic Games I: A ReductionVieille, N. Israel Journal of Mathematics, 119, 2000, 55-91.Two-Player Stochastic Games II: The Case of Recursive GamesVieille, N. Israel Journal of Mathematics, 119, 2000, 93-126.Small Perturbations and Stochastic GamesVieille, N. Israel Journal of Mathematics, 119, 2000, 127-142. [Link]


In this series of game theory papers, the author proves the existence of equilibrium payoffs for two-player stochastic games. He does this by first reducing the general stochastic games (introduced in 1953) to positive absorbing recursive games, and then by proving that in such a game there exists an equilibrium (which he refers to as the absorbing state). Although the existence of equilibrium payoffs in the non-zero-sum case was proven by Mertens and Newman in 1982, until this paper by Veille the non-zero-sum case remained unproven. As this problem has been worked on for about the last 50 years, this paper definitely represents a milestone in OR.


DECISION SCIENCE

http://80-newfirstsearch.oclc.org.ezproxy.library.arizona.edu/WebZ/FSQUERY?format=BI:next=html/records.html:bad=html/records.html:numrecs=10:sessionid=sp04sw05-51203-dnk0ycus-erodoe:entitypagenum=35:0:searchtype=advanced

http://80-newfirstsearch.oclc.org.ezproxy.library.arizona.edu/WebZ/FSQUERY?format=BI:next=html/records.html:bad=html/records.html:numrecs=10:sessionid=sp04sw05-51203-dnk0ycus-erodoe:entitypagenum=50:0:searchtype=advanced

CONTACT INFORMATIONStanford Graduate School of BusinessLittlefield 253518 Memorial WayStanford UniversityStanford, CA 94305-5015

(650) 723-0514 Telephone(650) 725-0468 [email protected]://gobi.stanford.edu/facultybios/bio.asp?ID=98 EDUCATIONPh.D. – Operations Research, University of Pennsylvania, 1983MS – Operations Research, University of Pennsylvania, 1979MSc – Operational Research, London School of Economics, 1975BSS – Economics and Statistics, University of Hong Kong, 1974

RESEARCH INTERESTSSupply chain management, Global logistic system design and control, Multi-echelon inventory systems, Manufacturing and distribution strategy, and Design for supply chain management

KEY PUBLICATIONS E-Fulfillment: Winning the Last Mile of E-Commerce, Sloan Management Review, 42, 4,

2001.


Hau Lee– Professor, Operations, Information, and Technology– Director, Stanford Global Supply Chain

Management ForumStanford University (Stanford, California)

DECISION SCIENCE

http://gobi.stanford.edu/facultybios/bio.asp?ID=98


http://www.onlineathens.com/

http://lsdis.cs.uga.edu/


Information distortion in a Supply Chain: The Bullwhip Effect, Management Science, 43, 4, 1997.

Variability Reduction Through Operations Reversal In Supply Chain Re-engineering, Management Science, 44, 2, 1998.

Supply Chain Inventory Management: Pitfalls and Opportunities, Sloan Management Review, vol. 33, 1992.

TRIVIA / MISCELLANEOUSLee designed and led instruction of a week-long Andersen Consulting training class called ‘Fulfill Demand’, which doctoral student Jeff Correll attended in January, 1999.

CONTACT INFORMATIONOperations & Information Management DepartmentThe Wharton School, University of Pennsylvania500 Jon M. Huntsman HallPhiladelphia, PA 19104-6340(215) 898.5872 Telephone(215) 898.3664 [email protected]://opim.wharton.upenn.edu/home/faculty/fisher/index.html EDUCATIONPh.D. – Operations Research, MIT, 1970SM - Management, MIT, 1969SB – Electrical Engineering, MIT, 1965

RESEARCH INTERESTSSupply Chain Management and Retailing


Marshall Fisher– UPS Transportation Professor for the Private Sector;

Professor of Operations and Information Management

– Co-Director, Fishman-Davidson Center for Service and Operations Management

The Wharton SchoolUniversity of Pennsylvania (Philadelphia,

DECISION SCIENCE

http://opim.wharton.upenn.edu/home/faculty/fisher/index.html


KEY PUBLICATIONS Supply Chain Inventory Management and the Value of Shared Information. Management

Science, 46, (August 2000), 1032-1050. What is The Right Supply Chain for your Product? Harvard Business Review, March/April

1997. Making Supply Meet Demand in an Uncertain World. Harvard Business Review, May/June

1994.

TRIVIA / MISCELLANEOUSAlong with two other co-authors, Fisher won the 1977 Lanchester Prize (awarded for the best contribution to operations research and the management sciences published in English) for his paper "Location on Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms".

CONTACT INFORMATIONDecision Sciences Department College of Business Administration University of Hawaii at Manoa2404 Maile WayHonolulu, HI 96744(808) 956-7082 Telephone (808) 956-9889 [email protected]://www.cba.hawaii.edu/sprague/home.htm

EDUCATIONPhD – Decision Sciences, Indiana University

RESEARCH INTERESTSDecision Support Systems, Strategic Systems Planning, Management of Information Systems, and Electronic Document Management

KEY PUBLICATIONS


Ralph Sprague– ProfessorDecision Sciences DepartmentUniversity of Hawaii at Manoa (Honolulu, Hawaii)

DECISION SCIENCE

http://www.cba.hawaii.edu/sprague/home.htm


A Framework for the Development of Decision Support Systems. MIS Quarterly, 4(4), 1980, 1-26.

Towards a Better Understanding of Electronic Document Management. HICSS , 5, 1996 , 53-61.

Electronic Document Management: Challenges and Opportunities for Information Systems Managers. MIS Quarterly, 19(1), 1995.

TRIVIA / MISCELLANEOUSSprague has served as Chairman or Co-Chairman of the Hawaii International Conference on System Sciences (HICSS) for the past 20 years.

CONTACT INFORMATIONUniversity of ConnecticutSchool of Business Administration368 Fairfield Rd, U-41 IMStorrs, CT 06269-2041(860) 486-4065 Telephone(860) 486-4839 [email protected]://www.business.uconn.edu/staff.asp?a=J_Marsden EDUCATIONJD – University of Kentucky, 1991PhD – Economics (Theoretical Econometrics), Purdue University, 1970MS – Economics, Purdue University, 1970AB – Economics, University of Illinois at Urbana-Champaign, 1967

RESEARCH INTERESTSInnovations in electronic markets, Knowledge-based systems, Legal issues in information technology, Quantitative methods and econometrics

KEY PUBLICATIONS Decision making Under Time Pressure with Different Information Sources and

Performance-Based Financial Incentives - Part I. Decision Support Systems, 34(1), 2002, 75-97.


James R. Marsden– Professor; Dept. Head; Shenkman Chair in e-Business; – Director, Connecticut Information Technology InstituteOperations and Information Management DepartmentUniversity of Connecticut (Storrs, Connecticut)

DECISION SCIENCE

http://www.business.uconn.edu/staff.asp?a=J_Marsden


http://www.uconn.edu/

http://www.business.uconn.edu/page.asp?id=1.2.6

http://www.business.uconn.edu/page.asp?id=1.2.6

http://www.citi.uconn.edu/

http://www.citi.uconn.edu/

http://www.hicss.hawaii.edu/

http://www.hicss.hawaii.edu/

http://www.informatik.uni-trier.de/~ley/db/conf/hicss/hicss1996-5.html#MeierS96

A Theory of Decision Support System Portfolio Design and Evaluation. Decision Support Systems, 9, 1993, 183-199.

TRIVIA / MISCELLANEOUSMarsden is a Departmental Editor (DSS Impacts and Evaluation) for Decision Support Systems and has served for several years as the Expert Systems Minitrack co-coordinator for the annual Hawaii International Conference on Systems Sciences.


CONTACT INFORMATIONFairleigh Dickinson UniversityDepartments of Computer Science/Information SystemsMetropolitan Campus 1000 River Road Teaneck, NJ 07666(201) 692-2119 Telephonevladimir_zwass @fdu.edu http://inside.fdu.edu/pt/zwass.html

EDUCATIONPhD – Computer Science, Columbia UniversityMS – Electrical Engineering, Moscow Institute of Energetics

RESEARCH INTERESTSInformation Systems, E-Commerce, and Network Communication

KEY PUBLICATIONS Electronic Commerce: Structures and Issues. International Journal of Electronic

Commerce, 1(1), 1996, 3-23. Foundations of Information Systems. (Irwin/McGraw-Hill, 1998).

TRIVIA / MISCELLANEOUSZwass founded both the Journal of Management Information Systems (JMIS – 1984) and the International Journal of Electronic Commerce (IJEC – 1996), two of the more respected journals in the field of MIS.


Vladimir Zwass- Distinguished Professor of Computer Science & Management Information Systems, Fairleigh Dickinson University

- Editor-in-Chief, Journal of Management Information Systems, International Journal of Electronic Commerce

DECISION SCIENCE

http://inside.fdu.edu/pt/zwass.html


Economics of Informatics

1985Pricing of

computer services

1987Theories of

electronic markets

1988Switching Costs

and Lock-in Theories

1993Productivity

paradoxof IT

1996Economics ofE-commerce

Business

1998Versioning ofinformation

1999Bundling ofInformation

goods

1999Economicsof Global IT

The economics of informatics sub-domain includes research regarding the effects of

economic theories on the application and use of IT within organizations. The most notable

economic effects of interest to MIS researchers are the creation and leveraging of economic

efficiencies, valuation methods, market tipping (and related phenomena), and transactions

costs.

Ronald Coase’s 1937 paper, “The Nature of the Firm,” is widely considered to be THE

fundamental work in this sub-domain, as it launched the start of virtually all economic

research that is applied to information technology in general. Coase’s work first proposed

the notion of “transactions costs” and illustrated the strategic importance of these costs to

all firms, regardless of market environment (competitive, oligopolistic, or monopolistic

markets). The research stream generated by this paper ultimately provided a sound

rationale for investments in technology development and fueled interest in (and demand for)

enterprise computing systems.

However, the economics of informatics sub-domain is not entirely dominated by transactions

cost research – although many research topics are a direct result of that work. The current

state of academic research in this area also includes in-depth explorations of:

- Bundling and pricing issues;- Switching costs;- Valuation methods (for systems, networks, installed bases, etc.);


ECONOMICS OF INFORMATICS

- Intellectual property rights;- Productivity assessments;- Electronic markets and their behavior;- Pricing and sharing of digital information; and- The evolution of IT as an enabler of organizational change.

The overwhelming breadth of research in this area makes a compact discussion impractical

– however, some researchers (and their research works) were cited with greater frequency

or emphasis over the course of our investigation. Thus, we have selected the following

papers as the sub-domain’s seminal works:

Bundling information goods: Prices, profits, and efficiencyBakos, Y., & Brynjolfsson, EManagement Science, 45(12) 1999. [Link]


Digital information goods have properties that distinguish them from traditional goods. The pricing strategy of digital information goods is discussed. Models bundling information goods are proposed and back by empirical analysis.

Information Technologies And Business Value - An Analytic And Empirical-investigation Barua, A.; Kriebel, C.; and Mukhopadhyay, TInformation Systems Research, 6(1) 1995, 3-23. [Link]


Contradictions about productivity gains from IT exist primarily due to problems with the measurement of data. A process oriented model to measure and understand the impact of IT is introduced.

The Productivity Paradox of Information TechnologyBrynjolsson, E.Communications of the ACM, 35(12) 1993, 66-67. [Link]


Productivity is the fundamental measure of a technology’s contribution. However, the relationship between productivity and technology is not understood. After observing the data of IT performance as explained in this article, much of the “productivity paradox” can be attributed to errors in measurement.



http://delivery.acm.org/10.1145/170000/163309/p66-brynjolfsson.pdf?key1=163309&key2=9292439601&coll=GUIDE&dl=GUIDE&CFID=14220083&CFTOKEN=90629287

http://www.gsia.cmu.edu/andrew/josee/www/qualifiers/is_seminar_tridas.html

http://ccs.mit.edu/papers/CCSWP199/

Evaluations of Strategic Investments in Information TechnologyClemons, E.K.Communications of the ACM, 34(1) 1991, 22-36. [Link]


Strategic application development is fundamentally different than back office automation. Strategic applications increase flexibility, responsiveness, and adaptability. Back office automation reduces expenses or increases capacity. Several cases and lessons learned about implementing strategic systems are presented in the paper.

Misinformation Systems Ackoff, R.L.Management Science, 14(4), Dec-67, b147-B157.


Common systems development assumptions are presented. The paper argues that these assumptions result in system deficiencies and that a management control system should be used to overcome the assumptions.

Dynamic competition with switching costsFarrell, J., & Shapiro, C.Rand Journal of Economics, 19, 1988, 123-137.


The paper argues that switching costs can cause inefficiency in a surprising way: far from forming an entry barrier, they encourage entry to serve new customers, even when such entry is inefficient.

The Impact of Information Systems on Organizations and MarketsGurbaxani, V.Communications of the ACM, 34(1), 1991, 59-73. [Link]


This paper builds on the existing economic theories of the agency theory and transaction and cost economics. A firm can use information systems (IS) to decentralize some decision rights and to centralize others, exploiting the merits of both systems and leading to a hybrid structure. When information technology (IT) plays a significant role in reducing internal coordination costs, a firm may find it advantageous to grow horizontally and vertically.




http://portal.acm.org/ft_gateway.cfm?id=99985&type=pdf&coll=portal&dl=ACM&CFID=14226019&CFTOKEN=4726321

Pricing Computer Services – Queuing EffectsMendelson, H.Communications of the ACM, 28(3), 1985, 312-321. [Link]


This article looks at the effects of queuing delays & the associated costs to the user. The paper provides a methodology for setting price, utilization, and capacity taking into account the value of users’ time.

Information Technology and Productivity: Evidence from Country-level DataDewan S., & Kraemer, K.Management Science, 46(4) 2000, 548-562. [Link]


This paper studies returns from IT investments, a key driver of the demand for the products and services of the global IT industry. An inter-country production function relating IT and non-IT inputs to GDP output is estimated, on panel data from 36 countries over the 1985-1993 period. Significant differences are found between developed and developing countries with respect their structure of returns from capital investments.

An Empirical Assessment of the Organization of Transnational Information SystemsKing, W. & Sethi, V.Journal of MIS, 15(4) 1999, 7-28. [Link]


This paper is based on five important dimensions of transnational strategy—the configuration of value chain activities, the coordination of value chain activities, centralization, strategic alliances, and market integration—to support the proposition that the organizational characteristics of centralization, dispersal, and coordination are differentially reflected in the IT configurations of various kinds of multinational corporations. In a centrally coordinated business structure, IT is also globally centralized. In addition, local autonomy was shown to affect the deployment of IT in global firms.

Information Technology and Economic Performance: A Critical Review of the Empirical EvidenceDedrick, J., Gurbaxani, V. & Kraemer, K.ACM Computing Surveys, 35(1) 2003, 1. [Link]


This paper critically reviews more than 50 published researches on computers and productivity, which show that the impact of IT investment on labor productivity and economic growth has been significant and positive in the last decade. It develops a general framework for classifying the research, which facilitates identifying what is known, how well it is known, and what is not known.



http://www.crito.uci.edu/publications/pdf/ITandEconPerf.pdf


http://www.crito.uci.edu/itr/PUBLICATIONS/PDF/ITR-126.PDF

http://delivery.acm.org/10.1145/10000/3171/p312-mendelson.pdf?key1=3171&key2=6792439601&coll=GUIDE&dl=ACM&CFID=14299632&CFTOKEN=72057335

Institutional Factors in Information Technology InnovationKing, J., Gurbaxani, V., Kraemer, K., McFarlan, F., et al.Information Systems Research, 5(2) 1994, 139-170. [Link]


This paper states that long-established intellectual perspectives on innovation from neoclassical economics and organization theory are not adequate to explain the dynamics of actual innovation change in the IT domain. A broader view adopted from economic history and the new institutionalism in sociology provides a stronger foundation for understanding the role of institutions in IT innovation. Institutional intervention in IT innovation can be constructed at the intersection of the influence and regulatory powers of institutions and the ideologies of supply-push and demand-pull models of innovation.

The Nature of the FirmCoase, Ronald .H.Economica, 4(16) 1937, 386-405. [Link]


This article proposes a clear and widely accepted theory suggesting why firms exist. It argues that transaction costs define the boundary of the firm. Transaction costs are the main factor that determine whether a transaction be conducted within the market or within the hierarchical organization. Lower transaction costs make the firm, planned economy, to be efficient and justify the existence of the firm.

Electronic Markets and Electronic HierarchiesMalone, T.W., Benjamin, R.I., and Yates, J. Communications of the ACM, 30(6) 1987, 484-497. [Link]


By reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets—rather than hierarchies—to coordinate economic activity.

Versioning: The smart way to sell informationShapiro, C. and Varian, H. R.Harvard Business Review, Nov-Dec 1998, 106-114. [Link]


Digital information products are subject to the laws of economics. After several companies have lowered production costs, competitive forces move marginal costs down. Low reproduction costs make products economically perilous. Success depends on traditional product-management skills: determining customer needs, achieving true differentiation, and developing an adept positioning and pricing plan.



http://infotrac.galegroup.com/itw/infomark/517/362/42118422w6/purl=rc1_GRGM_0_A53221402&dyn=5!xrn_14_0_A53221402?sw_aep=uarizona_main

http://portal.acm.org/citation.cfm?id=214762.214766&dl=GUIDE&dl=ACM&idx=214762&part=periodical&WantType=periodical&title=Communications%20of%20the%20ACM&CFID=14244226&CFTOKEN=49193665

http://people.bu.edu/vaguirre/courses/bu332/nature_firm.pdf


CONTACT INFORMATIONSloan School 50 Memorial Drive, E53-313 MIT Cambridge, MA 02142 (617) 253-4319 [email protected]://ebusiness.mit.edu/erik

EDUCATIONPh.D. – Managerial Economics, Massachusetts Institute of Technology, 1991SM – Math/Decision Science, Harvard University, 1984AB – Applied Mathematics, Harvard University, 1984

RESEARCH INTERESTSInformation Technology and Economics, including:

- Information technology and the organization of work- Information technology and productivity- Pricing and sharing of digital information

KEY PUBLICATIONS Brynjolfsson, E. (1993). “The Productivity Paradox of Information Technology.”

Communications of the ACM 35(12): 66-77. Brynjolfsson, E., & Hitt, L. (1996). Paradox lost? Firm-level evidence on the returns to

information systems spending. Management Science, 42(4), 541-558. Bakos, Y., & Brynjolfsson, E. (1999). “Bundling information goods: Prices, profits, and

efficiency.” Management Science, 45(12).


Erik Brynjolfsson– George and Sandi Schussel Professor of

Management– Director of the Center for eBusiness Sloan School of ManagementMassachusetts Institute of Technology (Cambridge,

MA)


http://ebusiness.mit.edu/erik


http://ebusiness.mit.edu/erik/photo.pdf

CONTACT INFORMATIONLeonard N. Stern School of BusinessNew York University44 West 4th Street, Room 8-83New York, NY 10012-1126(212) 998-0841 Telephone(212) 202-4130 [email protected] EDUCATIONPh.D. – Management, MIT Sloan School of ManagementMBA – Finance, MIT Sloan School of ManagementMS – Electrical Engineering, MIT Department of Electrical Engineering and Computer ScienceMS – Computer Science, MIT Department of Electrical Engineering and Computer ScienceBS – Computer Engineering, MIT Department of Electrical Engineering and Computer Science

RESEARCH INTERESTSEconomic and business implications of information technology, the Internet, and online media.

KEY PUBLICATIONS Bakos, Y., & Brynjolfsson, E. (1999). “Bundling information goods: Prices, profits, and

efficiency.” Management Science, 45(12). Bakos, Y. "Towards Friction-Free Markets: The Emerging Role of Electronic Marketplaces

on the Internet," Communications of the ACM, Volume 41, Number 8 (August 1998), pp. 35-42.

Bakos, Y. "A Strategic Analysis of Electronic Marketplaces," MIS Quarterly, Volume 15, No. 3, September 1991, pp. 295-310.


Yannis Bakos– Associate Professor of ManagementLeonard N. Stern School of BusinessNew York University (New York, New York)



http://pages.stern.nyu.edu/~bakos/bakos-max.jpg

CONTACT INFORMATIONGraduate School of BusinessHarvard UniversityCambridge, Massachusetts 02138(617) 495-6362 Telephone(617) 496-2910 Facsimile

[email protected]://pine.hbs.edu/external/facPersonalShow.do?pid=6411

EDUCATIONPh.D. – University of Arizona

RESEARCH INTERESTSInfluence of information technology on markets and organizations. Evolution of electronic commerce and role of IT as an enabler of flexible and adaptive organizational designs.

KEY PUBLICATIONS Applegate, L. M., Holsapple, C. W., Kalakota, R., Radermacher, F. J., and Whinston, A. B.

(1996). Electronic commerce: building blocks of new business opportunity. Journal of Organizational Computing and Electronic Commerce, 6(1), 1-10.

TRIVIA / MISCELLANEOUSLynda is a member of the Advisory Council for NASDAQ.


Lynda Applegate– ProfessorHarvard Business SchoolHarvard (Cambridge, Massachusetts)


http://pine.hbs.edu/external/facPersonalShow.do?pid=6411


CONTACT INFORMATIONGraduate School of BusinessLittlefield 253518 Memorial WayStanford UniversityStanford, California 94305(650) 725-8927 Telephone(650) 725-7979 [email protected]://gobi.stanford.edu/facultybios/bio.asp?ID=104

EDUCATIONPh.D. – School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel, 1979MSc – Management Sciences, Tel Aviv University, Tel Aviv, Israel, 1977BSc, Mathematics and Physics, Hebrew University, Jerusalem, Israel, 1972

RESEARCH INTERESTSElectronic business, electronic networks, and financial markets.

KEY PUBLICATIONS Mendelson, H. Pricing Computer Services – Queuing Effects. Communications of the ACM,

28, 3, (1985), 312-321. Mendelson, H. Economies of Scale in Computing: Grosch's Law Revisited.

Communications of the ACM, 30, (1987), 1066-1072. Mendelson, H. Incomplete Information Costs and Database Design. ACM Transactions on

Database Systems, 11, (1986), 159-185.


Haim Mendelson– General Atlantic Partners Professor of Electronic

Business and Commerce, and Management; – Codirector, Center for Electronic Business and

Commerce– Codirector, Strategic Uses of Information

Technology Executive Program Graduate School of BusinessStanford University (Stanford, California)


Mendelson, H. Clockspeed and Informational Response: Evidence from the Information Technology Industry. Information Systems Research, December 1998, pp. 415-433.

Mendelson, H. Organizational Architecture and Success in the IT Industry, Management Science, 2000

TRIVIA / MISCELLANEOUSDeveloped a measure of “organizational IQ” that quantifies the ability of a company or organization to make quick and effective decisions in the Internet age. He has also published over 70 journal articles.


CONTACT INFORMATIONUniversity of California, Irvine3200 Berkeley PlaceIrvine, California 92697(949) 824-5246 [email protected]://www.crito.uci.edu/kraemer.asp

EDUCATIONPh.D. – Public Administration, University of Southern California, 1967M.P.A. – Public Administration, University of Southern California, 1965M.S.C. & R.P. – City and Regional Planning, University of Southern California, 1964B. Architecture – University of Notre Dame, 1959

RESEARCH INTERESTSUse and Impact of Information Technology in Organizations; Globalization of Information Technology Production and Use; Management of Information Systems

KEY PUBLICATIONS Kraemer, K.; Dewan, S.; Information Technology and Productivity: Evidence from

Country-level Data. Management Science, 46, 4 (2000), 548-562. Kraemer, K.; King, J.; Institutional Factors in Information Technology Innovation.

Information Systems Research, 5, 2 (1994), 139-170.

TRIVIA / MISCELLANEOUSKenneth has served as Proposal Referee for The National Science Foundation from 1974 to present.He is also listed in American Men and Women in Science, Who’s Who in Technology Today, and Who’s Who in the West. (1985)


Kenneth L. Kraemer– Professor of Information Systems– Director of the Center for Research on Information

Technology and OrganizationsGraduate School of ManagementUniversity of California (Irvine, California)



CONTACT INFORMATIONE53-333 Massachusetts Institute of Technology Cambridge, MA 02139 (617) 253-6843 Telephone(617) 258-7579 [email protected]://ccs.mit.edu/Tom.html

EDUCATIONPh.D. – Stanford UniversityDegrees in Applied Mathematics, Engineering, and Psychology

RESEARCH INTERESTSHow new organizations can be designed to take advantage of the possibilities provided by information technology.

KEY PUBLICATIONS Malone, T.W., Benjamin, R.I., and Yates, J. (1987), “Electronic markets and electronic

hierarchies,” Communications of the ACM, 30(6), 484-497. Thomas Malone and Robert Laubacher (1998), “The Dawn of the E-Lance Economy,”

Harvard Business Review, Sept-Oct, 144-152...


Thomas W. Malone– Patrick J. McGovem Professor of Information

Systems – Director of the MIT Center for Coordination Science Sloan School of ManagementMassachusetts Institute of Technology (Cambridge,

MA)


http://ccs.mit.edu/Tom.html


CONTACT INFORMATIONUniversity of Chicago Law School1111 East 60th StreetChicago, IL 60637 USA(773) 702-7342 Telephone

EDUCATIONLondon School of Economics, 1929-1931.University of London, B. Com. 1932, D.Sc. (Econ.) 1951.University of Cologne, Dr. Rer. Pol. h.c., 1988.Yale University, D. So. Sc., (honorary) 1989.Washington University in St. Louis, LL.D., (honorary) 1991.University of Dundee, Scotland, LL.D., (honorary) 1992.University of Buckingham, England, D.Sci, (honorary) 1995.Beloit College, D.H.L., (honorary) 1996.Universite de Paris, docteur, (honoris causa) 1996.

RESEARCH CONTRIBUTIONSDr. Coase won the 1991 Nobel Prize in Economics "for his discovery and clarification of the significance of transaction costs and property rights for the institutional structure and functioning of the economy".

KEY PUBLICATIONS "The Nature of the Firm", 1937, Economica. "The Marginal Cost Controversy", 1946, Economica. "The Problem of Social Cost", 1960, Journal of Law and Economics.


Ronald H. Coase– Clifton R. Musser Professor Emeritus of Economics – Winner of the 1991 Nobel Prize in EconomicsUniversity of Chicago (Chicago, Illinois)


"Durability and Monopoly", 1972, Journal of Law and Economics. "The Lighthouse in Economics", 1974, Journal of Law and Economics. "Marshall on Method", 1975, Journal of Law and Economics. "The Wealth of Nations", 1977, Economic Inquiry. "Economics and Contiguous Disciplines", 1978, Journal of Legal Studies. "The New Institutional Economics", 1984, Journal of Institutional and Theoretical

Economics. “The Firm, the Market and the Law”, 1988. "The Institutional Structure of Production", 1992, AER "The Institutional Structure of Production", 1993, in Williamson, editor, Nature of the Firm.


CONTACT INFORMATIONThe University of Texas at AustinMgmt Sci & Info Systems1 University Station Stop B6500Austin, TX 78712(512) 471-8879 Telephone(512) 471-7962 [email protected]://cism.bus.utexas.edu/abw/main.html

EDUCATIONPh.D. – Management, Carnegie Mellon University, 1962

RESEARCH INTERESTSBringing technological, business, economic, public policy, sociological, cryptographic and political concerns together in laying the theoretical and practical foundations of a digital economy

KEY PUBLICATIONS Whiston, Andrew B., Stahl, Dale O., Choi, Soon-Yong. The Economics of Electronic

Commerce. Macmillan Technical Publishing, New York, New York. 1997.

TRIVIA / MISCELLANEOUSAndrew was named as the Distinguished Information Systems Educator in 1994 by the Data Processing Management Association.


Andrew B. Whinston–Director, Center for Research in Electronic

Commerce –Fellow IC2

– Professor MSIS, Computer Science, Economics DepartmentsUniversity of Texas (Austin, Texas)


http://cism.bus.utexas.edu/abw/main.html


Human-Computer Interface

June 1945Vannevar Bush's

"As We May Think"

1952Englebart begins

defining infomanipulation problems

1962Engelbart's

"AugmentingHuman Intellect"

framework

1963Sutherland's"SketchPad"Ph.D. Thesis

1965First

"computer mouse"unveiled (SRI)

1975David Canfield

Smith coinsterm "icons"

1977Xerox PARC

exploresWYSIWYG

displays

August 1977"ZOG: A

Man-MachineCommunication

Philosophy"(CMU)

1983Schniederman's

"Direct Manipulation"(IEEE Computer)

July 1962Licklider outlines"Man-ComputerSymbiosis" goals

Human-computer interaction (HCI) is an MIS research discipline that is primarily concerned

with the design, evaluation and implementation of interactive computing systems for human

use. HCI research draws heavily from many other academic disciplines, including the highly

technical fields of computer science and the highly behavioral fields of the social sciences.

As a result, the research in this area tends to be very diverse and offers countless

possibilities for specialization and/or application development.

Over the last few decades, much of the publicity for HCI-related innovation has been

incorrectly attributed to industry. Yet it is clear that academic research has given rise to the

development of the novel software, systems, and products that are being used today.

Furthermore, the ideologies and applications of this research is driving the development of

the next-generation computing tools on the horizon.

The very beginnings of HCI research are usually traced back to Vannevar Bush's 1945 article

entitled, "As We May Think," (Atlantic Monthly, July 1945, Vol. 176: pp. 101-108.) It was here

that Dr. Bush began to describe the information storage and retrieval problems that would

face the burgeoning computer industry in the future as computing became more

commonplace. He specifically states that "new knowledge does not reach the people who

could benefit most from it ... (rather), publication has been extended far beyond our present


HUMAN-COMPUTER INTERACTION

ability to make real use of the record.” This work fostered the idea that human concerns

must be taken into account when developing computer systems and applications if they are

to be effective and/or useable.

In the early 1950s, Douglas Engelbart extended Bush's ideology as he began to examine:

Information display & manipulation; the limitations of human abilities to process complex

problems or rich information; as well as the social effects of computing and collaboration.

Engelbart's breakthrough work in this area, "A Conceptual Framework for Augmenting

Human Intellect," was published in 1962. In it, he forms a more defined basis for future HCI

study -- saying that "by augmenting man’s intellect we mean increasing the capability of a

man to approach a complex problem situation, gain comprehension to suit his particular

needs, and to derive solutions to problems. One objective is to develop new techniques,

procedures, and systems that will better adapt people’s basic information handling

capabilities to the needs, problems, and progress of society."

J.C.R. Licklider, Ivan E. Sutherland, and Alan Kay each published a seminal work in HCI

during the decade of the 1960s. Licklider's visionary work outlined the "man-computer

symbiosis" that would eventually appear as the development of computing technologies

continued to evolve, and he proposed a series of goals that should be considered by the

parties that develop those tools. Sutherland's Ph.D. thesis introduced many concepts that

are now found in today's interfaces, including advanced input/output devices, hierarchical

design structures, and object-oriented programming principles. (Sutherland, I.E.

"SketchPad: A Man-Machine Graphical Communication System," in AFIPS Spring Joint

Computer Conference. 1963, vol. 23, pp. 329-346.) And Kay's 1969 cardboard prototype of

the laptop computer proved to be visionary in furthering the notion of personal computing

and inspiring the development that would revolutionize our society.



Today, HCI research could be organized into many interrelated sub-categories, including

(among others):

- The use and context of computers and systems;- The nature of human-computer interaction;- Human factors and characteristics;- Computer system and interface architecture;- Systems and software development;- Consumer product design and computing solutions; and- The visualization and usability of data/information.

The scope of (and distinctions between) these sub-categories are highly fluid and tend to

change often. As such, attempts to further sub-classify the watershed HCI research is well

beyond the scope of this paper. Nonetheless, a sample of the key academic research works

considered to be the cornerstones of the myriad HCI research variants include:

Organizational Information Requirements, Media Richness and Structural DesignDaft, R. L., Lengel, R. H.Management Science, 32 (5). 1986. 554-571. [Link]


This paper answers the question, "Why do organizations process information?" Uncertainty and equivocality are defined as two forces that influence information processing in organizations. Organization structure and internal systems determine both the amount and richness of information provided to managers. Models are proposed that show how organizations can be designed to meet the information needs of technology, interdepartmental relations, and the environment. One implication for managers is that a major problem is lack of clarity, not lack of data. The models indicate how organizations can be designed to provide information mechanisms to both reduce uncertainty and resolve equivocality.

The Vocabulary Problem in Human-System CommunicationFurnas, G. W., Landauer, T. K., and Gomez, L. M.Communications of the ACM, 30 (11). 1987. 964-971. [Link]


In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training, or in first-tries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five application-related domains, and found the variability to be surprisingly large. In every case two people favored the same term with probability <0.20. Simulations show how this fundamental property of language limits the success of various design methodologies for vocabulary-driven interaction. For example, the popular approach in which access is via one designer's favorite single word will result in 80-90 percent failure



http://portal.acm.org/citation.cfm?id=32206.32212&dl=GUIDE&dl=ACM&CFID=14771747&CFTOKEN=66768465

http://www.jstor.org/browse/00251909/di012854/01p0357u?config=jstor&frame=noframe&[email protected]/01cc99333c0050fcb19d&dpi=3

rates in many common situations. An optimal strategy, unlimited aliasing, is derived and shown to be capable of several-fold improvements.


Information Foraging in Formation Access EnvironmentsPirolli, P. and Card, S.CHI 1995: ACM Conference on Human Factors in Software / ACM Press. 1995. 51-58. [Link]


Information foraging theory is an approach to the analysis of human activities involving information access technologies. The theory derives from optimal foraging theory in biology and anthropology, which analyzes the adaptive value of food-foraging strategies. Information foraging theory analyzes trade-offs in the value of information gained against the costs of performing activity in human-computer interaction tasks. The theory is illustrated by application to information-seeking tasks involving a Scatter/Gather interface, which presents users with a navigable, automatically computed, overview of the contents of a document collection arranged as a cluster hierarchy.

The Usability Engineering Life CycleNielsen, J.IEEE Computer, 25 (3). 1992. 12-22. [Link]


A practical usability engineering process that can be incorporated into the software product development process to ensure the usability of interactive computer products is presented. It is shown that the most basic elements in the usability engineering model are empirical user testing and prototyping, combined with iterative design. Usability activities are presented for three main phases of a software project: before, during, and after product design and implementation. Some of the recommended methods are not really single steps but should be used throughout the development process.

Direct Manipulation: A Step Beyond Programming LanguageShneiderman, B.IEEE Computer. 1993. 57-69.


Schneiderman's theory of direct manipulation describes interactive systems where a user manipulates the files and folders within their system by using methods other than typed commands. Direct manipulation systems also display a visual representation of the system's activity or progression, which result in an increase in the user's perception of control. The central themes of user control are rooted in: The visibility of system objects and actions; rapid, reversible, incremental actions; and the replacement of complex command syntax with simple visual alternatives.

A Language/Action Perspective on the Design of Cooperative WorkWinograd, T.Human-Computer Interaction, 3 (1). 1988. 3-30. [Link]


This paper examines the language/action perspective on the design of cooperative work. It includes: A description of the coordinator communication tool from a language/action



http://portal.acm.org/citation.cfm?id=637096&jmp=cit&dl=GUIDE&dl=ACM&CFID=11111111&CFTOKEN=2222222

http://ieeexplore.ieee.org/iel1/2/3459/00121503.pdf?isNumber=3459&prod=IEEE+JNL&arnumber=121503&arSt=12&ared=22&arAuthor=Nielsen,+J.;


perspective; aspects of coordinated work; and an illustration of the language/action perspective in the studies of nursing work in a hospital ward.


Computer-Mediated Communication for Intellectual TeamworkGalegher J., Kraut, R.E.Information Systems Research, 5 (2). 1994. 110-138.


This paper focuses on a study which examined the effects of computer-mediated communication on group processes and performance. It includes a manipulation of communication modality; association of evaluations and work activities; and discusses the effects of communication modality on social experience.

Generalized Fisheye ViewsFurnas, G. W.CHI 1986: ACM Conference on Human Factors in Software / ACM Press. 1986. 16-23. [Link]


In many contexts, humans often represent their own “neighborhood” in great detail, yet only generalize major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.




EDUCATIONPh.D. – Engineering, Harvard and MIT, 1916-1917MS – Tufts College, 1913BS – Tufts College

RESEARCH INTERESTSHypertext research.

KEY PUBLICATIONS Bush, Vannevar. (1945) As We May Think. The Atlantic Monthly, Vol. 176, No. 1. 101-108.

[Link]

TRIVIA / MISCELLANEOUSIn his watershed article (“As We May Think”), Vannevar's conception of the "Memex" introduced, for the first time, the idea of an easily accessible, individually configurable storehouse of knowledge.

Vannevar served as: President of the Carnegie Institute in Washington, DC (1939); chair of the National Advisory Committee for Aeronautics (1939); and director of the Office of Scientific Research and Development – a presidential appointment which made him responsible for the 6,000 scientists involved in the war effort during WWII.


Stuart K. Card– Senior Research Fellow – Area Manager, User Interface Research (UIR) Group Information Sciences & Technologies LaboratoryPalo Alto Research Center, Inc. (Palo Alto, California)

Vannevar Bush (1890-1974)– Director, U.S. Office of Scientific Research &

Development– Vice President and Dean School of EngineeringMassachusetts Institute of Technology (Cambridge,

MA)


http://www.theatlantic.com/unbound/flashbks/computer/bushf.htm

CONTACT INFORMATIONPalo Alto Research Center, Incorporated3333 Coyote Hill RoadPalo Alto, California 94304(650) 812-4000 [email protected] (or [email protected])

EDUCATIONPh.D. – Psychology/AI/Computer Science, Carnegie Mellon University, 1978 (Chair: Allen Newell)AB – Physics, Oberlin College, 1966

RESEARCH INTERESTSHuman factors for input devices (e.g. mice), information visualization, and information scent.

KEY PUBLICATIONS Card, S., Moran, T., & Newell, A. (1983). The Psychology of Human-Computer Interaction.

Hillsdale, NJ: Lawrence Erlbaum. Newell, A. & Card, S. K. (1985). The Prospects for Psychological Science in Human-

Computer Interaction. Human-Computer Interaction 1(3). 209-242. Card, S., Mackinlay, J., & Shneiderman, B. (1999). Readings in Information Visualization:

Using Vision to Think. Morgan Kaufmann Publishers.








In 2000, Stu was awarded the first Lifetime Achievement Award for CHI and became a Fellow in the Association for Computing Machinery. In 2001, he became the first fellow of the CHI Academy.

Stu has written three books, more than 70 papers, and holds 22 issued patents. He has developed models in human-computer interaction, including: The Model Human Processor, the GOMS theory of user interaction, Fitts’ Law model of the mouse, and information foraging. He has also developed new user interface techniques, such as Rooms and the “focus + context” information visualization methods.

In 1985 and 1988 he was a lead member in ACM's SIGCHI Curriculum Committee, which prescribed the course of study for computer and human interfaces for university education.


CONTACT INFORMATIONBootstrap Institute6505 Kaiser DriveFremont, CA 94555 (510) 713-3550 Telephone(510) 792-3506 Facsimilehttp://www.bootstrap.org/index.html

EDUCATIONPh.D. – Electrical Engineering, University of California-Berkeley, 1955BS – Electrical Engineering, Oregon State University, 1948

RESEARCH INTERESTSAugmenting human intellect – via graphical design, human factors, and hypertextual information display.

KEY PUBLICATIONS Englebart, D.C. (October 1962). Augmenting Human Intellect: A Conceptual Framework.

Summary Report, Stanford Research Institute, on Contract AF 49(638)-1024. [Link] English, William, Engelbart, Douglas, & Berman, Melvyn. (March 1967) Display-Selection

Techniques for Text Manipulation. IEEE Transactions on Human Factors in Electronics, HFE-8: 1, 5-15. [Link]

TRIVIA / MISCELLANEOUSDouglas worked at the NACA-Ames Laboratory (the precursor of NASA) upon completion of his undergraduate work – he left because he felt he wasn’t making significant contributions to mankind.

Douglas invented the mouse at Stanford Research Labs in 1964.

In 1968, at the Fall Joint Computer Conference in San Francisco, Englebart demonstrated his hypertext-based NLS (oNLineSystem) in a 90 minute multimedia presentation that included a live video conference with staff members back in his lab 30 miles away. To this day, Englebart's demo is still known as "the mother of all demos." Some people in attendance thought the whole thing was a hoax. [Link]



Douglas C. Engelbart– Director, Augmentation Research, SRI – Founder, Bootstrap Institute Stanford Research LabsStanford University (Stanford, California)


http://www.cs.umd.edu/class/spring2002/cmsc434-0101/MUIseum/people/englebart.html

http://www.bootstrap.org/augdocs/friedewald030402/display1967/Display1967.html

http://www.bootstrap.org/augdocs/friedewald030402/augmentinghumanintellect/ahi62index.html

http://www.bootstrap.org/index.html

CONTACT INFORMATIONSchool of Information University of Michigan 310 West Hall 550 East University AvenueAnn Arbor, Michigan 48109-1092(734) 763-0076 Telephone(734) 764-2475 [email protected]://www.si.umich.edu/~furnas/

EDUCATIONPh.D. – Psychology, Stanford University, 1980BA – Psychology, Harvard University, 1974

RESEARCH INTERESTSHCI: information access, visualization, computer-supported cooperative work & graphical reasoning.

KEY PUBLICATIONS Furnas, G., Landauer, T., & Gomez, L. M. (1987) The Vocabulary Problem in Human-

System Communication. Communications of the ACM, 30 (11), 964-971. Furnas, G., (1986) Generalized Fisheye Views. CHI 1986: ACM Conference on Human

Factors in Software / ACM Press, 16-23.


George W. Furnas– Professor, School of Information– Professor, Computer Science and EngineeringThe University of Michigan (Ann Arbor, Michigan)


http://www.si.umich.edu/~furnas/


Furnas, G.W. (1997) Effective View Navigation. Proceedings of CHI 1997: Human Factors in Computing Systems.

TRIVIA / MISCELLANEOUSGeorge earned the Distinguished Member of Technical Staff Award at Bell Communications Research in 1988.


INFORMATIONhttp://www.ibiblio.org/pioneers/licklider.html

EDUCATIONPh.D. – Psychoacoustics (the psychophysiology of the auditory system), MITBA – Mathematics, Washington State UniversityBS – Physics, Washington State UniversityBS – Psychology, Washington State University

RESEARCH INTERESTSHuman-computer interaction.

KEY PUBLICATIONS Licklider, J.C.R. (March 1960) Man-Computer Symbiosis. IRE Transactions on Human

Factors in Electronics, HFE-1. 4–11. [Link] Licklider, J.C.R. (April 1968) The Computer as a Communication Device. Science and

Technology.

TRIVIA / MISCELLANEOUS"Man-Computer Symbiosis" was inspired by an informal experiment Lick conducted with himself as the subject. (He decided to keep track of how he spent his time at work.)



J.C.R. Licklider (1915-1990)– Founding DirectorInformation Processing Techniques OfficeAdvanced Research Projects Agency (Washington,

DC)


http://www.ipo.tue.nl/homepages/mrauterb/presentations/licklider.pdf

http://www.ibiblio.org/pioneers/licklider.html

CONTACT INFORMATIONHuman Computer Interaction InstituteCarnegie Mellon University5000 Forbes AvenuePittsburgh, PA 15213-3891(412) 268-5150 Telephone(412) 268-1266 [email protected]://www-2.cs.cmu.edu/~bam/

EDUCATIONPh.D. -- Computer Science, University of Toronto, 1987. MS -- Computer Science, Massachusetts Institute of Technology, 1980BS -- Computer Science and Engineering, Massachusetts Institute of Technology, 1980.

RESEARCH INTERESTSUser Interface Software, Hand-held computers, Demonstrational Interfaces, User Interface Design, Window Managers, Visual Programming, Programming Environments.

KEY PUBLICATIONS Brad A. Myers. Creating User Interfaces by Demonstration. Boston, MA: Academic Press,

May 1988. Brad A. Myers, ed. Languages for Developing User Interfaces. Boston: Jones and Bartlett,

1992.



Brad A. Myers– Associate Research Professor, School of Computer

ScienceHuman Computer Interaction InstituteCarnegie Mellon University (Pittsburgh, Pennsylvania)


http://www-2.cs.cmu.edu/~bam/


Myers, Brad A. (March 1998) A Brief History of Human Computer Interaction Technology. ACM interactions, 5(2). 44-54..

TRIVIA / MISCELLANEOUSBrad was listed in the Outstanding Scientists of the 20th Century (2000) by the International Biographical Centre (Cambridge, England).

Brad has authored or edited over 240 publications, and he is on the editorial board of five journals.


EDUCATIONPh.D. – Industrial Administration, Carnegie Institute of Technology (now CMU), 1957BA – Physics, Stanford University, 1949

RESEARCH INTERESTSComputer science, artificial intelligence, and cognitive psychology.

KEY PUBLICATIONS Newell, A. (1990) Unified Theories of Cognition. Cambridge, MA: Harvard University

Press. Card, S.K., Moran, T.P., & Newell, A. (1980) The Keystroke-Level Model for User

Performance Time with Interactive Systems. Communications of the ACM 23(7), 396-410.

Robertson, G., Newell, A., & Ramakrishna, K. (1977) ZOG: A Man-Machine Communication Philosophy. Carnegie Mellon University Technical Report, August 1977.

Newell, A. and Simon, H.A. (1976) Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM 19(3), 113-126.

TRIVIA / MISCELLANEOUSAllen contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theory Machine (1956) and the General Problem Solver (1957).


Allen Newell (1927-1992)– Professor, School of Information– Professor, Computer Science and EngineeringCarnegie-Mellon University (Pittsburgh, Pennsylvania)


Each year, the ACM/AAAI presents an award (named after Allen) to an individual for career contributions that have breadth within computer science, or that bridge computer science and other disciplines.

In a speech delivered at CMU seven months before his death, Allen described his career as aimed single-mindedly at understanding the human mind, but he also confessed to four or five substantial diversions from that goal – almost all of which produced major scientific products of their own. These "diversions" included work on computer hardware architectures, the psychology of human-computer interaction, and a major advisory role in the ARPA program of research on speech recognition.Allen's work aimed steadily at using computer simulation as the key research tool for understanding and modeling the human mind. After the first burst of activity, which produced the Logic Theorist, the General Problem Solver, and the NSS chess program, he focused increasingly on identifying and overcoming the limitations and inflexibilities of these models that impeded their extension into a wholly general theory of the mind. [The last two paragraphs are excerpts of a tribute to Allen written by Herb Simon in 1992.].


CONTACT INFORMATIONNielsen Norman Group48921 Warm Springs BoulevardFremont, California 94539-7767(408) 720-8808 [email protected]://www.nngroup.com/

EDUCATIONPh.D. – User Interface Design, The Technical University of Denmark

RESEARCH INTERESTSWeb usability, interface design, information architecture, and task design.

KEY PUBLICATIONS Nielsen, J. (2000) Designing Web Usability: The Practice of Simplicity. Indianapolis, IN:

New Riders Publishing. Nielsen, J. (1992). Finding Usability Problems Through Heuristic Evaluation. Proceedings

of the ACM CHI:1992, 373-380. Nielsen, J., and Faber, J. M. (1996). Improving System Usability Through Parallel Design.

IEEE Computer, 29, 2 (February). 29-35.


Jakob Nielsen– Co-Founder & principalNielsen Norman Group (Fremont, California)


http://www.nngroup.com/


TRIVIA / MISCELLANEOUSUntil 1998, Jakob was a Sun Microsystems Distinguished Engineer. Jakob is a self-proclaimed "User Advocate" and founded the "discount usability engineering" movement (for fast and cheap improvements of user interfaces).Jakob has invented several usability methods, including heuristic evaluation. As a result, he holds 73 United States patents, mainly on ways of making the Internet easier to use. .


CONTACT INFORMATIONNielsen Norman Group2841 Manor DriveNorthbrook, Illinois 60062(847) 498-4292 Telephone(847) 272-6631 [email protected]://www.nngroup.com/

EDUCATIONPh.D. – Mathematical Psychology, University of Pennsylvania, 1962.MS – Electrical Engineering, University of Pennsylvania, 1959.BS – Electrical Engineering, Massachusetts Institute of Technology, 1957.

RESEARCH INTERESTSThe human-centered design process; physical objects with embedded computation and telecommunication.

KEY PUBLICATIONS Norman, D. A. and Draper, S. (1986) User Centered System Design: New Perspectives on

Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum. Norman, D.A. (1990) The design of everyday things. New York: Doubleday.


Donald A. Norman– Co-Founder & principal, Nielsen Norman Group– Professor, Depts. of Computer Science &

Psychology, Northwestern University.– Professor emeritus, Cognitive Science, University of

California - San Diego.– Trustee, Institute of Design, Illinois Institute of

Technology.


http://www.nngroup.com/


TRIVIA / MISCELLANEOUSDon is a fellow of: American Association of Arts and Sciences, American Psychology Society, Association for Computing Machinery (ACM), Cognitive Science Society, as well as the Human Factors and Ergonomics Society.

Don was named one of Upside Magazine's “Elite 100” in 1999, earned SIGDOC's Rigo award for “outstanding life-time contribution to the field of user documentation,” in 2001, and received CHI's Lifetime achievement award in 2002.

Don is a former VP of research at Apple Computer.


CONTACT INFORMATIONDepartment of Computer ScienceUniversity of Maryland College Park, MD 20742(301) 405-2680 Telephone(301) 405-6707 [email protected]://www.cs.umd.edu/users/ben/

EDUCATIONPh.D. – Computer Science, State University of New York at Stony Brook, 1973MS – Computer Science, State University of New York at Stony Brook, 1972BS – Mathematics/Physics, City College of New York, 1968

RESEARCH INTERESTSHuman-computer interaction and user interface design.

KEY PUBLICATIONS Schneiderman, B. (1993, August). Direct manipulation: A Step Beyond Programming

Languages. IEEE Computer 16, 8, 57-69. Schneiderman, B. (1987). Designing the User Interface: Strategies of Effective Human-

Computer Interaction (3rd ed.). Pearson Addison Wesley. Schneiderman, B. (1980). Software Psychology: Human Factors in Computer and

Information Systems. Little, Brown and Co.


Ben Schneiderman– Professor, Department of Computer Science– Member, Institute for Systems Research– Member, Institute for Advanced Computer Studies– Founding Director, Human-Computer Interaction

LabDepartment of Computer ScienceThe University of Maryland (College Park, Maryland)


http://www.cs.umd.edu/users/ben/


TRIVIA / MISCELLANEOUSBen was made a Fellow of the ACM in 1997, elected a Fellow of the American Association for the Advancement of Science in 2001, and received the ACM CHI Lifetime Achievement Award in 2001.

Ben's is also known in software engineering for his widely used innovation of structured flowcharts, commonly known as Nassi-Shneiderman Diagrams.

Ben has co-authored two textbooks, edited three technical books, and published more than 200 technical papers & book chapters.


CONTACT INFORMATIONStanford University Gates Computer Science 3B, Room 388 Stanford, California 94305-9035(650) 723-2780 Telephone(650) 723-0033 [email protected]://hci.stanford.edu/~winograd/

EDUCATIONPh.D. – Applied Mathematics, Massachusetts Institute of Technology, 1970MA -- Linguistics, University College (London), 1967BA – Mathematics, The Colorado College, 1966

RESEARCH INTERESTSHuman-computer interaction design, with a focus on the theoretical background and conceptual models.

KEY PUBLICATIONS Winograd, T., (1988) A Language/Action Perspective on the Design of Cooperative Work.

Human-Computer Interaction, 3 (1), 3-30. Winograd, T., Understanding Natural Language, Academic Press, 1972. Winograd, T., Language as a Cognitive Process: Syntax, Addison-Wesley, 1983.


Terry A. Winograd– Professor, Department of Computer Science– Director, Human-Computer Interaction teaching

programs– HCI Research Director, Stanford Interactivity LabDepartment of Computer ScienceStanford University (Stanford, California)


http://hci.stanford.edu/~winograd/


Winograd T. & Flores, F., Understanding Computers and Cognition: A New Foundation for Design Addison-Wesley, 1987.

Adler, P. & Winograd, T. (eds.), Usability: Turning Technologies into Tools Oxford, 1992.Winograd, T., Bennett, J., De Young, L., & Hartfield, B. (eds.), Bringing Design to Software, Addison Wesley, 1996.


Social Informatics

1988Adaptive technology

required forteam differences

1994Fair use

and digital data

1998Trust in globalvirtual teams

2000Framework to

study technologyin organizations

2001Intellectual property

in an openinformation environment

Social Informatics (SI) investigates the effects of information technology on social issues.

These social issues can be divided into two categories: policy issues and the social impact of

computerization. Examples of policy issues include legislation and company computer

policies. Examples of the social impact of computerization include computer-mediated

communication and organizational informatics.

Policy issues have come to the forefront of public debate in the recent past as several pieces

of high-profile legislation have arisen to combat piracy, copyright violations, fraud and

terrorism. Social Informatics will continue to be an extremely active research field as these

and other issues are explored.

Within the context of our model, the key social informatics papers include:

Electronic Markets and Electronic HierarchiesMalone, Thomas W., Yates, Joanne, Benjamin, Robert I.Communications of the ACM, 30(6), 1987, 484-497. [Link]


Electronic interconnections can be seen as the result of three forces: the electronic communication effect, the electronic brokerage effect, and the electronic integration effect. By reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets rather than hierarchies to coordinate economic activity.


SOCIAL INFORMATICS

http://infotrac.galegroup.com/itw/infomark/660/721/67696075w3/purl=rc1_GRGM_0_A6366361&dyn=17!xrn_5_0_A6366361?sw_aep=uarizona_main

Thinking about ImplementationSproull, L.S., Hofmeister, K.Journal of Management, 12(1), 1986, 43-60. [Link]


Ideas from cognitive psychology are used to explore the role of different perceptions, attributions and inferences in how people think about implementation. Some perceptions, attributions and inferences shifted over time, but initial major differences associated with organizational position and commitment to the innovation did not change.

Reducing Social Context Cues: Electronic Mail in Organizational CommunicationSproull, L., Kiesler, S.Management Science, 32(12), 1986, 1492-1512. [Link]


Electronic mail speeds up communication and leads to the exchange of new information as well. This paper explores the effects of electronic communication related to self-absorption, status equalization, and uninhibited behavior. Decreasing social context cues has a substantial deregulating effect on communication. Much of the information shared in electronic mail would not have been conveyed through any other medium.

Toward a New Politics of Intellectual PropertySamuelson, P.Communications of the ACM, 44(3) 2001, 98-99. [Link]


Traditionally copyright was on the periphery of law because it involved technical rules for a highly specialized industry. Copyright law has therefore become highly complex and effectively unreadable. A new politics of intellectual property is proposed due to the fact that copyright deeply affects the information environment for us all. Articulating a positive case for an open information environment is probably the single most important thing the new politics of intellectual property might do.

Copyright's Fair Use Doctrine and Digital DataSamuelson, P.Communications of the ACM, 37(1) 1994, 21-27. [Link]


The application of copyright law’s Fair Use doctrine to works in digital form is discussed. The fair use doctrine provides a flexible and adaptable way to balance the interests of copyright owners and of the public so as to maintain adequate incentives to produce creative works while at the same time allowing the public to make reasonable uses of copyrighted materials. Several court cases are examined to illustrate how courts are likely to analyze the fairness of certain uses in the U.S.


SOCIAL INFORMATICS

http://delivery.acm.org/10.1145/180000/175233/p21-samuelson.pdf?key1=175233&key2=3958269601&coll=portal&dl=ACM&CFID=14305674&CFTOKEN=58405090

http://www.sims.berkeley.edu/~pam/papers/CACMNewPolitics3.pdf

http://search.epnet.com/direct.asp?an=7347959&db=heh&site=ehost


Using Technology and Constituting Structures: A Practice Lens for Studying Technology in OrganizationsOrlikowski, W.Organization Science, 11(4) 2000, 404-428. [Link]


A proposal is made of an extension to the structurational perspective on technology that develops a practice lens to examine how people, as they interact with a technology in their ongoing practices, enact structures which shape their emergent and situated use of that technology. After developing this lens, an example of its use in organizational research is offered, and then some implications for the study of technology in organizations are suggested.

Is Anybody Out There? Antecedents of Trust in Global Virtual TeamsJarvenpaa, S., Knoll K., Leidner, D.Journal of MIS, 14(4) 1998, 29-64. [Link]


Seventy-five teams, consisting of members in different countries, are studied to explore the antecedents of trust in a global virtual team setting. The two-week trust-building exercises did have a significant effect on the team members’ perceptions of the other members’ ability, integrity, and benevolence. A qualitative analysis of e-mails explores the strategy of “swift” trust. A research model for explaining trust in global virtual teams is then advanced.

Computer Support for Meetings of Groups Working On Unstructured Problems: A Field ExperimentJarvenpaa, S., Rao, V., Huber, G.MIS Quarterly, 12(4) 1988, 645-666. [Link]


A preliminary study was conducted to investigate the consequences of computer support for teams working on unstructured, high-level conceptual software design problems in face-to-face group settings. A networked workstation technology and electronic blackboard technology were contrasted with conventional communication technology. Significant team differences were found in performance and interaction measures. The results indicate that the theory of compute-based meeting support technology must be extended to account for team differences.

Computerization and Social TransformationsKling, RobScience Technology and Human Values, 16 (3), 1991, 342-367. [Link]


This article examines the relationship between the use of computer-based systems and transformations in parts of the social order. Answers to this question rest heavily on the way computer-based systems are consumed -not just produced or disseminated. The article examines qualitative case studies of computerization in welfare agencies, urban planning, accounting, marketing, and manufacturing to examine the ways that computerization alters


SOCIAL INFORMATICS

http://links.jstor.org/sici?sici=0162-2439(199122)16:3%3C342:CAST%3E2.0.CO;2-B




social life in varied ways: sometimes restructuring relationships and in other cases reinforcing existing social relationships. The article also examines some of the theoretical issues in studies of computerization, such as drawing boundaries.

Institutional Factors in Information Technology InnovationKing, J.L., Gurbaxani, V., Kraemer, K.L.Information Systems Research, 5(2), 1994, 139-169. [Link]


A research model is developed that links two major dimensions of IS planning – the quality of the planning process and planning effectiveness with a set of eight organizational factors derived from the contingency research in IS planning. The model is validated from surveys from senior IS executives. The “ends” and “means” of IS planning are equally important.

Critical Success Factor Analysis as a Methodology for MIS PlanningShank, Michael E.; Boynton, Andrew C.; Zmud MIS Quarterly, 9(2), 1985, 121-129. [Link]


This article addresses the use and benefits of the Critical Success Factor (CSF) methodology in identifying corporate information needs and, subsequently, in developing a corporate information systems plan. The outcome of the CSF study has been a fundamental rethinking of the nature of the corporation, and its impact far surpassed the initial expectations of everyone involved. The case presented here, combined with information drawn from the CSF literature, can provide a number of meaningful insights on the use of the CSF methodology as a procedure for MIS planning and for building support for using information technologies throughout a user population.


SOCIAL INFORMATICS

http://links.jstor.org/sici?sici=0276-7783(198506)9:2%3C121:CSFAAA%3E2.0.CO;2-6


CONTACT INFORMATION

Information/Decision Sciences3-341 CarlSMgmt321 19th Avenue SouthMinneapolis, MN 55455(612) 624-2523 Telephone(612) 626-1316 [email protected]://www.carlsonschool.umn.edu/Page2075.aspx?type=faculty&eid=105545305

EDUCATIONPh.D. – Business Administration, Stanford University, 1959M.B.A – Business Administration, Stanford University, 1957B.S – Political Science and Accounting, Idaho State University, 1955

RESEARCH INTERESTSMIS planning, information requirements determination, management of knowledge work, conceptual foundations for information systems

KEY PUBLICATIONS Davis, G., Hamilton, S., Managing Information: How Information Systems Impact

Organizational Strategy, Business One Irwin, 1993

TRIVIA / MISCELLANEOUSGordon served as the president of the Association for Information Systems in 1998 and was named an ACM Fellow in 1996.


Gordon Davis– Professor of Information and Decision SciencesCarlson School of ManagementUniversity of Minnesota (Minneapolis, Minnesota)

SOCIAL INFORMATICS

http://www.carlsonschool.umn.edu/Page2075.aspx?type=faculty&eid=105545305


CONTACT INFORMATIONSam Nunn School of International AffairsGeorgia Institute of Technology781 Marietta St. NWAtlanta, GA 30318-5750(404) 385-1461 Telephone(404) 894-1900 [email protected]://www.inta.gatech.edu/goodman/index.html

EDUCATIONPh.D. – Applied Mathematics and Mathematical Physics, California Institute of Technology (1970)BS – Engineering, Columbia University (1965)

RESEARCH INTERESTSInternational Dimensions of Information Technologies and Related Policy Issues; Technology Diffusion; Information Technologies and National Security

KEY PUBLICATIONS S. E. Goodman, P. Wolcott and G. Burkhart, An Examination of High-Performance

Computing Export Control Policy in the 1990s. IEEE Computer Society Monograph, Los Altos CA, 1996, 115 pages.

Zixiang Tan, William Foster and Seymour Goodman, Chinas State-Coordinated Internet Infrastructure, Comm. of the ACM, Vol. 42, No. 6, June 1999, pp. 44-52.

Seymour E. Goodman, James B. Gottstein, Diane S. Goodman, Wiring the Wilderness in Alaska and the Yukon, Comm. of the ACM, Vol. 44, No. 6, June 2001, pp. 21-25.

TRIVIA / MISCELLANEOUSSeymour serves as the International Perspectives Editor for the Communications of the ACM and is listed in the Marquis Who's Who in the World and Who's Who in America and won the World Technology Award for Law in 2001.


Seymour Goodman– Professor of International Affairs and Computing– Co-Director of Georgia Tech Information Security

Center– Co-Director of Center for International Strategy,

Technology and PolicyCollege of Computing & Sam Nunn School of Intl.

Affairs

SOCIAL INFORMATICS


CONTACT INFORMATIONDepartment of Management Science & Information SystemsMcCombs School of BusinessUniversity of TexasAustin, TX 78712-1178(512) 471-1751 Telephone(512) 232-6112 [email protected]

http://www.mccombs.utexas.edu/dept/msis/faculty/profiles/index.asp?addTarget=316

EDUCATIONPh.D. – University of Minnesota, 1986MBA – University of Minnesota, 1982 BS – Bowling Green State University, 1981

RESEARCH INTERESTSClicks and Mortars, Customer Insight, E-Commerce, and Information Systems.

KEY PUBLICATIONS "Business Process Redesign: Radical and Evolutionary Change," (with D.B. Stoddard),

Journal of Business Research, 1998 "Is Anybody Out There?: The Antecedents of Trust in Global Virtual Teams," (with K. Knoll

and D. Leidner), Journal of Management Information Systems, 1998 Reengineering the Organization (with R. Nolan, D. Stoddard and T. H. Davenport) ,

Harvard Business School Press , 1995

TRIVIA / MISCELLANEOUSSirkka is the Associate Editor for ACM Transactions on Human Computer Interaction and sits on the Advisory Board for the Journal of AIS.


Sirkka L. Jarvenpaa– James L. Bayless/Rauscher Pierce Refsnes, Inc. Chair in Business AdministrationMcCombs School of BusinessUniversity of Texas (Austin, Texas)

SOCIAL INFORMATICS

http://www.mccombs.utexas.edu/dept/msis/faculty/profiles/index.asp?addTarget=316

EDUCATIONPh.D. – Artificial Intelligence, Stanford University, 1971Undergraduate – Columbia University, 1965

RESEARCH INTERESTSSocial informatics; the effective use of electronic media to support scholarly and professional communication

KEY PUBLICATIONS Kling, R.; Computerization and Social Transformations. Science, Technology and Human

Value, 16, 3 (1991), 342-367.

TRIVIA / MISCELLANEOUSRob was the Editor-in-Chief of The Information Society and earned the Silver Core Award from the International Federation of Information Processing Societies in 1983.


Rob Kling (1944-2003)– Professor of Information Systems and Information

Science– Director of the Center for Social Informatics– Adjunct Professor of Computer ScienceSchool of Library and Information ScienceIndiana University at Bloomington

SOCIAL INFORMATICS

CONTACT INFORMATION

Stanford Law SchoolCrown Quadrangle559 Nathan Abbott WayStanford, CA 94305-8610(650) 736-0999 [email protected]://www.law.stanford.edu/faculty/lessig/

EDUCATIONJD – Yale, 1989MA – Philosophy, Trinity College, 1986BS – Management, University of Pennsylvania, 1983BA – Economics, University of Pennsylvania, 1983

RESEARCH INTERESTSLaw of Cyberspace; Internet Society; Architectures of identity; Open sources;

KEY PUBLICATIONS Lessigh, L; The Future of Ideas: The Fate of the Commons in a Connected World, Random

House, 2001. Lessigh, L; Code, and Other Laws of Cyberspace, Basic Books, 1999.

TRIVIA / MISCELLANEOUSLawrence was named one of Scientific American's Top 50 Visionaries and was listed in Business Week's "25 Top eBiz Leaders" in 2000 & 2001. He also earned the World Technology Award for Law in 2001.


Lawrence Lessigh– Professor of Law– Founder of the Stanford Center for Internet and

SocietyStanford Law SchoolStanford University (Stanford, California)

SOCIAL INFORMATICS


CONTACT INFORMATIONMIT Sloan School 50 Memorial Drive (E53-325) Cambridge, MA 02142-1347

(617) 253-0443 Telephone(617) 258-7579 [email protected]

http://ccs.mit.edu/Wanda.html

EDUCATIONPh.D. – Stern School of Business, New York University, 1985

RESEARCH INTERESTSDynamic interactions between organizations and information technology

KEY PUBLICATIONS Orlikowski, W.J.; Using Technology and Constituting Structures: A Practical Lens for

Studying Technology in Organizations. Organization Science, 11, 4 (2000), 404-428.TRIVIA / MISCELLANEOUSWanda is a member of the Academy of Management, the Association of Computing Machinery, the Institute of Management Science, the Society of Information Management, and the Society for Organizational Learning.


Wanda J. Orlikowski– Professor of Information Technologies and

Organization Studies– Eaton-Peabody Chair of Communication Sciences Sloan School of ManagementMassachusetts Institute of Technology

SOCIAL INFORMATICS

http://ccs.mit.edu/Wanda.html


CONTACT INFORMATIONUniversity of California at Berkeley 102 South Hall Berkeley, CA 94720-4600

(510) 642-6775 Telephone(510) 642-5814 [email protected]://www.sims.berkeley.edu/~pam/

EDUCATIONJ.D. – Yale Law School, Yale University, 1976M.A. – Political Science, University of Hawaii at Honolulu, 1972B.S. – History, University of Hawaii at Honolulu, 1971

RESEARCH INTERESTSIntellectual property law, public policy for information technology and traditional legal regimes

KEY PUBLICATIONS Samuelson, P.; Copyright’s Fair Use Doctrine and Digital Data. Communications of the

ACM, 37, 1 (1994), 21-27. Samuelson, P.; Toward a New Politics of Intellectual Property. Communications of the

ACM, 44, 3 (2001), 98-99.TRIVIA / MISCELLANEOUSOne of the twenty-five "most intriguing minds of the new economy" in the inaugural issue of Business 2.0 in July 1998


Pamela Samuelson– Professor– Co-Director of Berkeley Center for Law and

Technology School of Information Management and SystemsSchool of LawUniversity of California (Berkeley, California)

SOCIAL INFORMATICS


CONTACT INFORMATIONStern School of BusinessNew York University44 West 4 th Street, 11-55 New York, NY 10012212-998-0804 – Telephone212-995-4228 – FAX

[email protected]

http://pages.stern.nyu.edu/~lsproull/

EDUCATIONPh.D. – Stanford University, 1977M. A. – Stanford University, 1975M.A.T – Wesleyan University, 1969BA – Wellesley College, 1967

RESEARCH INTERESTSImplications of computer-based communication technologies for managers, organizations, communities, and society and how technology induces changes in interpersonal interaction, group dynamics and decision making, and organizational or community structure.

KEY PUBLICATIONS Lee Sproull and Kay Hofmeister. 1986. Thinking about implementation. Journal of

Management, 12: 43-60. Lee Sproull. 1986. Using electronic mail for data collection in organizational research.

Academy of Management Journal, 29: 159-169. Lee Sproull. 1984. The nature of managerial attention. Advances in Information

Processing in Organizations, Vol. 1: 9-27. Greenwich, CT: JAI Press.

TRIVIA / MISCELLANEOUSLee Sproull has been a member of both Beta Kappa Sigma and Sigma Xi.


Lee S. Sproull– Leonard N. Stern School Professor,– Vice-Dean of the Faculty– Director, Digital Economy InitiativeStern School of BusinessNew York University (New York, New York)

SOCIAL INFORMATICS

http://pages.stern.nyu.edu/~lsproull/


CONTACT INFORMATION

Division of MISMichael F. Price College of BusinessUniversity of OklahomaNorman, OK 73019-4006(405) 325-0791 Telephone(405) 325-1957 [email protected]://faculty-staff.ou.edu/Z/Robert.W.Zmud-1/

EDUCATIONPh.D. – Management, University of ArizonaM.S. – Management, Massachusetts Institute of TechnologyB.A.E. – Aerospace Engineering, University of Virginia

RESEARCH INTERESTSThe impact of information technology in facilitating a variety of organizational behaviors and on organizational efforts involved with planning, managing, and diffusing information technology.

KEY PUBLICATIONS Cooper, R.; Zmud, R. Information Technology Implementation Research: A Technology

Diffusion Approach. Management Science, 34(2), 1990, 123-139. Shank, Michael E.; Boynton, Andrew C.; Zmud, Robert W. Critical Success Factor Analysis

as a Methodology for MIS Planning. MIS Quarterly, 9(2), 1985, 121-129.

TRIVIA / MISCELLANEOUSRobert graduated from the Ph.D. program at the University of Arizona.


Robert W. Zmud– Editor-in-Chief and Senior Editor of MIS Quarterly– Professor and Michael F. Price Chair in MIS University of Oklahoma

SOCIAL INFORMATICS

http://faculty-staff.ou.edu/Z/Robert.W.Zmud-1/


System Analysis and Design

1968General Systems

Theory

1972Information

Hiding

1979Structured

Design

1980Abstraction

1985-1986Object-Oriented

Development emerges1994-1997

UML takes shape

1980workflowemerges

1993Workflow

managementcoalition founded

Several decades ago, to build software system just means to grab a computer and type the

code out. Software development means nothing more than programming, or coding. As the

size of software systems becomes larger and larger, the complexity of implementing it

makes people realize that it is better they think about how to do it before they want to get

their hands dirty. That is what we now call system analysis and design.

Since a complex system is no longer possible to be delivered by a single person, group of

programmers are involved. The communication among them becomes a new problem. How

to let everybody in the team stay at the same stage as the development goes? What if a

person has to leave the project, how can others pick up his work and keep the flow go? As

more and more systems become legacy systems, how can people do about it and keep it

usable? Or, on the other hand, how to let people twenty years from now understand and

use what you build today? What if the customer requirements change a bit, do we need to

start it over or, if we think about it carefully enough beforehand, maybe we just need to

twist it a little?

All these questions are to be answered within the area of system analysis and design. In the

70s’, structured design is the dominating design approach in the software industry. Edward

Yourdon published the Structured Design, Englewood Cliffs, NJ, Prentice-Hall, in 1979.

Sequential structure is the most popular way to decompose a complicated system. Systems


SYSTEMS ANALYSIS & DESIGN

are cut into the pieces based on their functionalities. And each functional piece is developed

independently. This approach is replaced gradually by a later coming design methodology

that represents a totally different way of thinking about the system, the way of objects. In

1986, Grady Booch published Object-Oriented Development. He, Jacobson, and Rumbaugh,

referred as the gang of three, formalized the object-oriented analysis and design. In the mid

80s’, as the appearance of Java, the object-oriented design begins to take over what

sequential structure had. This new generation of people is so aggressive and ready to turn

the world up side down that they prefer everything to be different. When others say

functions, they say operations.

Nowadays, the World Wide Web becomes the most splendid phenomenon. It is almost

affecting every aspect of human’s life. Is it going to change the SAD methodology again, or

has it already? There is one thing for sure. As long as computer and software stay in our

life, to think of how to make it better and easier to build software will always be worth a lot

of sleepless nights for us.

Within the context of our model, the ten key Workflow papers include:

Object-Oriented DevelopmentGrady BoochIEEE Transactions On Software Engineering, 12(2), 1986, 211-221


This paper serves as a complete introduction of the object-oriented design and implementation. The definition, attributes, operations and properties of the object is described in detail. The object-oriented system design is compared with the traditional structural approach. ADA is mentioned as a implementation tool for object-oriented design. Finally, a design case study is carefully addressed.

This paper could be treated as a milestone of the object-oriented design approach.

Anchoring the software processBarry BoehmIEEE Software, July 1996. [Link]




http://csdl.computer.org/comp/mags/so/1996/04/s4073abs.htm

Risk management is one of the most important activities in software engineering. Project often fails because there is no common anchor points around which to plan and control. This article identifies three milestones -- Life Cycle Objectives, Life Cycle Architecture, and Initial Operational Capability -- which can serve as these common anchor points.

A Spiral Model of Software Development and EnhancementBarry BoehmIEEE Computer, 21(5): 61-72, 1988. [Link]


This is the seminar paper in spiral software development model. An evolving risk-driven approach is what most today’s projects follow and this paper describes the spiral software process models, the issues it address and primary difficulties in using it.

Managing Large Software ProjectsTom DeMarco, Ann MillerIEEE Software, 13(4), 1996, 24-27. [Link]


This article is an introduction of the management of large software projects in the software industry. Because of the positive economy of scale in software development, large systems are worth substantially more when they are built and may even built at a smaller unit cost.

Software Risk ManagementBarry W. Boehm, Tom DeMarcoIEEE Software, 14(3), 1997 17-19. [Link]


This article serves as an introduction of the risk management in the software industry. Risk concerns and how project managers handle them in the real world are vividly expressed. At last, several continuing challenges are addressed.

Java, the Web, and Software DevelopmentEdward YourdonIEEE Computer 29(8), 1996, 25-30. [Link]


This paper analyses the impact of the World Wide Web, and usage of Java on the software development. Based on author’s view, he predicts that a brand-new Internet-based applications development architecture will dominate the traditional client-server mode. Several key issues of the application development are also discussed.



http://ieeexplore.ieee.org/iel1/2/11233/00532042.pdf?isNumber=11233&prod=IEEE+JNL&arnumber=532042&arSt=25&ared=30&arAuthor=Yourdon,+E.;

http://ieeexplore.ieee.org/iel4/52/12938/00589225.pdf?isNumber=12938&prod=JNL&arnumber=589225&arSt=17&ared=19&arAuthor=Boehm,+B.W.;+DeMarco,+T

http://ieeexplore.ieee.org/iel1/52/11027/00526827.pdf?isNumber=11027&prod=JNL&arnumber=526827&arSt=24&ared=&arAuthor=DeMarco,+T.;+Miller,+A

http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=59

Program Development by Stepwise Refinement N. Wirth Communications of the ACM 14(4), April 1971, 221-227. [Link]


This paper proposes the programming methodology of stepwise refinement. This methodology advocates for making a program by following a sequence of steps. Initially, a vague program is designed. By following a sequence of steps, the programmer faces at each step one design decision about the program construction. Finally, an executable program is obtained.

The Draco approach to constructing software from reusable componentsJ. M. NeighborsIEEE Transactions on Software Engineering 10(5), 1984, 564-74. [Link]


This paper discusses an approach called Draco to the construction of software systems from reusable software parts. The particular approach organizes reusable software components by problem area or domain. Statements of programs in these specialized domains are then optimized by source-to-source program transformations and refined into other domains. Draco was the first system to support the transformation of high-level domain specific programs to executable code.

Inconsistency Handling in Multperspective SpecificationsAnthony Finkelstein, Dov M. Gabbay, Anthony Hunter, Jeff Kramer, Bashar NuseibehIEEE Trans. Software Eng. 20(8): 569-578 (1994). [Link]


This paper proposed a new solution to handle the multiple perspectives and inconsistencies in the development of large and complex systems. This new approach brings together two promising lines of research: multi-perspective software develpment in the ViewPoints frame-work and inconsistency handling, using classical and action-based temporal logics, and thus provides a sound alternative approach to software development.

Dynamic Configuration for Distributed SystemsJeff Kramer; Jeff MageeIEEE Trans. Software Eng. 11(4): 424-435 (1985).


This paper presents a basic model of the configuration process which permits dynamic incremental modification and extension. The properties required by languages and their execution environments in order to support dynamic configuration are determined by this model, through the experiment on CONIC, the distributed system which has been developed at Imperial College.



http://www.bayfronttechnologies.com/itt83p.pdf

http://portal.acm.org/ft_gateway.cfm?id=362577&type=pdf&coll=GUIDE&dl=ACM&CFID=14366939

CONTACT INFORMATIONUSC Center for Software EngineeringUniversity of Southern CaliforniaLos Angeles, CA 90007(213) 740-8163 Telephone(706) 740-4927 [email protected]://sunset.usc.edu/Research_Group/barry.html

EDUCATIONPh.D. – Mathematics, UCLA, 1964M.S. – Mathematics, UCLA, 1961BA – Mathematics, Harvard, 1957

RESEARCH INTERESTSSoftware process modeling, Software requirement engineering, Software architecture, Software metrics and cost models, Software engineering environment, Knowledge-based software engineering

KEY PUBLICATIONS Barry Boehm, J.R. Brown, H. Kaspar, M. Lipow, “Characteristics of Software Quality”,

Elsevier Science, 1978. Barry Boehm, "Anchoring the Software Process", IEEE Software, July 1996. Barry Boehm, A. Egyed, J.Kwan "Developing Multimedia Applications with the WinWin

Spiral Model," Proceedings, ESEC/FSE 97 and ACM Software Engineering Notes, November 1997.


Barry Boehm– Professor, Computer Science– Director, Center for Software EngineeringUniversity of Southern California (Los Angeles, CA)


http://sunset.usc.edu/Research_Group/barry.html



CONTACT INFORMATIONThe Atlantic Systems GuildCamden, MEPhone: (207) [email protected]://www.systemsguild.com/GuildSite/TDM/TDMBio.html EDUCATIONBSEE - Cornell UniversityM.S. - Columbia University Diplome - University of Paris at the Sorbonne

RESEARCH INTERESTSSystem software specification, Risk management, Project management, Requirement engineering, and Litigation of software-intensive contracts.

KEY PUBLICATIONS Tom DeMarco, Timothy Lister. “Peopleware: Productive Projects and Teams”, Victory

Audio Video Services, October 1993. Tom DeMarco. “Structured analysis and system specification”, Prentice-Hall, 1979. Tom DeMarco. “Controlling software projects : management, measurement &

estimation”, Yourdon Press, 1982.

TRIVIA / MISCELLANEOUSTom DeMarco is an Emergency Medical Technician, certified by his home state and by the National Registry of EMTs.


Tom DeMarco - Principal, The Atlantic Systems Guild- Fellow of the Cutter Consortium.


http://www.systemsguild.com/GuildSite/TDM/Litigation.html

http://www.systemsguild.com/GuildSite/TDM/TDMBio.html

CONTACT INFORMATIONDistributed Software Engineering Department of ComputingImperial College180 Queen’s GateLondon SW7 2AZ UNITED KINGDOM44-20-7594-8271 Telephone44-20-7594-8282 [email protected]://www.doc.ic.ac.uk/~jk/

EDUCATIONBS – Electrical Engineering, University of Natal, South Africa, 1970MS – Computer Science, Imperial College, London, England, 1972PhD – Computer Science, Imperial College, London, England, 1979

RESEARCH INTERESTSDistributed systems: configuration languages and support environments; distributed algorithms for fault tolerance, load sharing and dynamic configuration; multi-agent systems.

Software architectures: structural concepts, underlying theory, and application to the specification and construction of concurrent and distributed software systems: Darwin Architectural Description Language.

KEY PUBLICATIONS Jeff Kramer, Jeff Magee: Dynamic Configuration for Distributed Systems. IEEE Trans.

Software Eng. 11(4): 424-436 (1985). Anthony Finkelstein, Dov M. Gabbay, Anthony Hunter, Jeff Kramer, Bashar Nuseibeh:

Inconsistency Handling in Multperspective Specifications. IEEE Trans. Software Eng. 20(8): 569-578 (1994).

Kramer, J., Magee, J., and Sloman, M.S.,"Managing Evolution in Distributed Systems".IEE Software Engineering Journal , 4 (6), November 1989, 321-329.

Kramer,J., and Magee,J.,"The Evolving Philosophers Problem: Dynamic Change Management", IEEE Trans. on Software Eng., SE-16 (11), (1990), 1293-1306.


Jeff Kramer - Professor and Head, Department of Computing- Head, Distributed Software Engineering Research

CenterImperial College (London, England)


http://www-dse.doc.ic.ac.uk/research/architecture.html

http://www.doc.ic.ac.uk/~jk/


CONTACT INFORMATION37 BroadwayArlington, Massachusetts 02474(212) 214-0775 Telephone(212) 214-0775 [email protected]://www.yourdon.com EDUCATIONBS – Applied Mathematics, MIT

RESEARCH INTERESTSEdward Yourdon is widely known as the lead developer of the structured analysis/design methods of the 1970s, and was a co-developer of the Yourdon/Whitehead method of object-oriented analysis/design and the popular Coad/Yourdon OO methodology in the early 1990s. He has worked in the computer industry for 38 years. During his career, he has worked on over 25 different mainframe computers, and was involved in a number of pioneering computer technologies such as time-sharing operating systems and virtual memory systems.

KEY PUBLICATIONS The Practical Guide to Structured System Design Englewood Cliffs, N.J. Prentice-Hall,

1975. Structured Design. Englewood Cliffs, NJ: Prentice-Hall, 1979.

TRIVIA / MISCELLANEOUSEdward Yourdon is the author or co-author of 26 books and more than 560 technical papers.


Edward Yourdon- Independent Computer Consultant, Author, and

Lecturer- Chairman of Cutter Consortium


http://www.yourdon.com/

mailto:%[email protected]

Workflow

Workflow is a relatively new MIS research classification that has emerged as a subcategory

under the broader Systems Analysis and Design umbrella. Although the first Workflow

papers began appearing as early as 1980, the vast majority of publications in this area have

occurred over the past decade (see SAD timeline). As businesses continue to show interest

in the productivity benefits and efficiency gains from Workflow applications, this trend of

Workflow as an expanding MIS research area is likely to continue for the foreseeable future.

Workflow is a research domain that combines elements of people, processes, and

information technologies. As such, throughout its brief history it has been somewhat

difficult to define and nearly impossible to fully categorize. Nonetheless, the Workflow

Management Coalition (WFMC) has defined Workflow as ‘the automation of a business

process, in whole or part, during which documents, information or tasks are passed from one

participant to another for action, according to a set of procedural rules’. Because the WFMC

is the primary authoritative organization in this field, we chose to use their working

definition as the foundation of the Workflow section of our model, and thus as the basis for

the selection of the most important Workflow papers, as well as the key researchers (see

Appendix).

Within the context of our model, the three key Workflow papers include:

Office Information Systems and Computer ScienceEllis, C.A.; Nutt, G.J.ACM Computing Surveys, 12(1), 1980. [Link]


Arguably one of the first papers focused on Workflow. Businesses have many tasks that can be automated individually, but the challenge is to integrate the individual components in order to reduce complexity, control the flows of information, and enhance the overall efficiency of the business.


WORKFLOW

http://portal.acm.org/ft_gateway.cfm?id=356805&type=pdf&coll=GUIDE&dl=ACM&CFID=14236536&CFTOKEN=27320669

Process ModelingCurtis, B.; Kellner, M.I.; Over, J.Communications of the ACM, 35(9), 1992, 75-90. [Link]


This paper illustrates process modeling from functional, behavioral, organizational, and informational perspectives. The authors explain five dominant process modeling paradigms: programming models, functional models, plan-based models, Petri-net models, and quantitative models. Additionally, the authors suggest several future Workflow research focus areas: multi-paradigm representations, Workflow usage in process improvement, and process-based software development environments.

An Overview of Workflow Management: From Process Modeling to Workflow Automation InfrastructureGeorgakopoulos, D.; Hornick, M.; Sheth, A.Distributed and Parallel Databases, 3, 1995, 119-153. [Link]


This paper provides a high level overview of workflow management methodologies and software products. In addition, the authors also discuss the emerging infrastructure technologies that will support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. Specifically, the article provides a discussion of how distributed object management and customized transaction management can support further advances in Workflow.


WORKFLOW

http://citeseer.nj.nec.com/georgakopoulos95overview.html

http://delivery.acm.org/10.1145/140000/130998/p75-curtis.pdf?key1=130998&key2=4298639601&coll=portal&dl=ACM&CFID=14314037&CFTOKEN=22754110

CONTACT INFORMATIONLarge Scale Distributed Information System LabDepartment of Computer ScienceUniversity of Georgia415 Graduate Studies Research CenterAthens, GA 30602-7404(706) 542-2310 Telephone(706) 542-4771 [email protected]://lsdis.cs.uga.edu/~amit/

EDUCATIONPh.D. – Computer & Information Science, Ohio State University, 1985MS. – Computer & Information Science, Ohio State University, 1983BE – Electrical & Electronics Engineering, Birla Institute of Tech. & Science, 1981

RESEARCH INTERESTSSemantic Web and Semantic Information Brokering, Semantic Web Processes (including Dynamic Trading Processes in E-Commerce, Multi-organization Business Processes and Workflow Management, Work Coordination and Collaboration Systems), Management of Rich Media Content and Information Sources, and Semantic Applications in Financial, Healthcare and National Security

KEY PUBLICATIONS Sheth, C. Bertram, D. Avant, B. Hammond, K. Kochut, Y. Warke, Semantic Content

Management for Enterprises and the Web, IEEE Internet Computing, July/August 2002, pp. 80-87.


Amit P. Sheth– Professor– Director, Large Scale Distributed Information System LabDepartment of Computer ScienceUniversity of Georgia (Athens, Georgia)

WORKFLOW

http://lsdis.cs.uga.edu/~amit/


http://www.onlineathens.com/

http://www.uga.edu/




K. Shah and A. Sheth, "InfoHarness: An Information Integration Platform for Managing Distributed, Heterogeneous Information," IEEE Internet Computing, November-December 1999, p. 18-28.

N. Krishnakumar and A. Sheth, “Managing Heterogeneous Multi-system Tasks to Support Enterprise-wide Operations,” Distributed and Parallel Databases Journal, 3 (2), April 1995, pp. 155-186.

TRIVIA / MISCELLANEOUSSheth serves as the CTO and co-founder of Semagix, Inc (formerly Voquette, Inc). Sheth has also founded another high-tech company—Infocosm, Inc.


http://www.semagix.com/

CONTACT INFORMATIONDepartment of Computer ScienceEngineering Center ECOT 747430 UCBBoulder, CO 80309-0430(303) 492-5984 Telephone(303) 492-2844 [email protected]://www.cs.colorado.edu/~skip/Home.html

EDUCATIONPh.D. – Computer Science, University of Illinois, 1969BS - Math and Physics, Beloit College in Wisconsin

RESEARCH INTERESTSWorkflow Technology, Groupware, Cognitive Science (Group cognition), Computer Supported Cooperative Work, OO Systems, Systems Modeling, Distributed Interaction Systems, and Group User Interfaces.

KEY PUBLICATIONS Ellis, C.A. "Goal Based Workflow Systems" International Journal of Collaborative

Computing, 1(1), 1994, 61-86. Ellis, C.A., Nutt, G.J. “Office Information Systems and Computer Science” ACM Computing

Surveys, 12(1), 1980.

TRIVIA / MISCELLANEOUSEllis was the first African-American to receive a PhD in Computer Science.


Clarence (Skip) EllisDepartment of Computer ScienceUniversity of Colorado (Boulder, Colorado)

WORKFLOW

http://www.cs.colorado.edu/~skip/Home.html


http://www.colorado.edu/

http://www.cs.colorado.edu/

ACKNOWLEDGEMENTS

Our class would like to express our sincere thanks to the following individuals for their contributions to this project:

- Dr. Jay F. Nunamaker, University of Arizona

- Dr. Randy Boyle, University of Alabama in Huntsville

- Dr. Hsing (Kenny) Chen, University of Florida

- Jason Deane, University of Florida

- Dr. Moshe Dror, University of Arizona

- Dr. Therani Madhusudan, University of Arizona

- Dr. David E. Pingry, University of Arizona

- Chris Steinmeyer, University of Arizona

- Dr. Jay M. Teets, University of Florida

- Dr. Sherry M.B. Thatcher, University of Arizona

- Dr. Daniel Zeng, University of Arizona

- Dr. J. Leon Zhao, University of Arizona


Model of MISand

Leading Research

Prepared By:Chris Diller

Hoon Cha Matt Jensen Tom MeservyWei Chang Sidd Kaza Rob Schumaker

Cuiping Chen Jun Liu Wei WeiJeff Correll Ying Liu Ming Yuan

Sanghu Gite Iljoo Kim Mike ZhongJoel Helquist Jian Ma Ling Zhu


MIS 696-A – Readings in MISDr. Jay F. Nunamaker

10 December 2003


Documents

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