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SOFT MODELINGSUPPORT FORMANAGINGKNOWLEDGE-BASEDINFORMATIONTECHNOLOGY (IT)PROJECTSCezary OrlowskiPublished online: 30 Nov 2010.

To cite this article: Cezary Orlowski (2002) SOFT MODELING SUPPORTFOR MANAGING KNOWLEDGE-BASED INFORMATION TECHNOLOGY (IT)PROJECTS, Cybernetics and Systems: An International Journal, 33:4,401-411, DOI: 10.1080/01969720290040669

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SOFT MODELING SUPPORT FOR MANAGINGKNOWLEDGE-BASED INFORMATIONTECHNOLOGY (IT) PROJECTS

CEZARY ORLOWSKI

Faculty of Management and Economics,Technical University of Gdansk, Poland

The present article aims to present the self-adjusting fuzzy model of the software

engineering management, which is going to aid in creating knowledge-based

systems. Carrying out such systems creates problems for managers of project

teams, which in turn is connected with a limited knowledge of the subject

matter, lack of IT tools for the acquisition, and implementation of knowledge

and the coordination of the cooperation between experts and engineers. Thus,

the solutions that aid the project management processes, especially those related

to the changes, risk and the timeof realization, are sought. The suggested model,

which is based on the knowledge and theory of regulators and fuzzy sets, might

offeran answer to theaboveproblems. Whilebuilding the system, theknowledge

of managing the real software systems was used and created conditions for the

tuning of the model, building knowledge-base rules and membership functions.

Managing IT projects is a complex research and utilitarian problem as it

integrates solutions from a number of areas related to software engineer-

ing: methods, techniques and tools of information science, management

science, and system science. Paulk et al. (1995) de®ne IT project man-

agement as an organized set of processes designed to deliver a product, i.e.

an IT system, under speci®c conditions. Serrano (1987) claims that IT

project management contains all management functions: leadership,

control, planning, and organization. According to Beyond-Davies (1999),

Address correspondence to Cezary Orlowski, Technical University of Gdansk,

Faculty of Management and Economics, Narutowicza 11=12, 80– 952 Gdansk, Poland.

E-mail: cor@zie.pg.gda.pl

Cybernetics and Systems: An InternationalJournal, 33: 401–411, 2002

Copyright # 2002 Taylor & Francis

0196-9722/02 $12.00 + .00

DOI: 10.1080/01969720290040669

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IT project management involves three inter-dependent stages: planning,

organization, and control, all designed to deliver a speci®ed IT system.

The term ``knowledge-based system’’ is often associated with IT

projects, and has different meaning for different authors (Baborski 1994;

Ceri and Fraternali 1997; Ullman 1988). In this paper it represents a class

of IT systems with a rule-object knowledge representation and mixed

(forward and backward) type of deduction. The IT knowledge-based

systems have an increasingly important effect on the global economy and

call for comprehensive research, technical analysis of implementation

cases, and involvement of specialists from various areas. This means

substantial expenditure for companies involved in IT systems develop-

ment and implementation, high risk, and equally high expectations of

signi®cant bene®ts by making the businesses more attractive in our

increasingly competitive market (Laudon and Laudon 1991).

The IT project management approach as presented in the paper

attempts to use qualitative (soft) modeling support in the process of

systems development. This approach looks at project management as the

management of a group of pre-de®ned design processes which have to be

completed within the assumed time and by using the available resources.

MODEL OFA KNOWLEDGE-BASED IT PROJECT MANAGEMENTSYSTEM

The paper presents a systems approach to the problem of planning and

control of IT projects. The approach is based on the theory of modeling

and process simulation (Zeigler 1984), design theory (Braha and Maimon

1998), and the concurrent approach to systems analysis and development

(Szczerbicki 1997; Szczerbicki and Punch 2001). The proposed solution

was integrated through the use of IT and Arti®cial Intelligence (AI) tools

and techniques.

IT projects that include knowledge management require a result-

oriented approach, organizationa l and project risk assessment, control

over how design work proceeds, and full consideration of how new meth-

odological and qualitative types of solutions are developed (Go rski 1999).

The solutions presented use empirical data gathered by project teams.

For the purpose of this paper, an IT project is de®ned in multiple

scopes, levels, and platforms using the concept of state space. The proposed

approach to project management involves four main stages as depicted

in Figure 1.

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In stage one a description of the real life system is given. Stage two

presents hierarchical structure of project management model. Stage three

describes the structural model, and stage four provides characteristics of

the integrated model. The integrated model combines components

of fuzzy models of analysis and synthesis processes, non-fuzzy processes

of formal control requirements, control of planning processes, and con-

trol of IT project management.

The real life system consists of a team working in IT environment

and using available techniques and tools to develop a speci®ed end

product ± a software package. This system is presented using observable

and non-observable variables. Measurable variables include input and

output data. According to Zeigler (1984), an experimental framework

presents a ®nite set of conditions for observing the real system or

implementing the experiment. For the implementation reasons a sub-set

of the real life system’s input-output responses was de®ned in the adopted

approach. A comparative criterion was developed and used to de®ne

input-output pairs corresponding to the structure of the real life system.

The hierarchical architecture of stage two and structural model of

stage three are depicted in Figure 2 and Figure 3, respectively. Relations

Figure 1. The integrated IT project management model: development stages.

MANAGING KNOWLEDGE-BASED IT PROJECTS 403

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Figure 2. Hierarchical model of IT project management.

Figure 3. Structural model of IT project management.

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between input and output data of the structural model are de®ned using

the concept of state space (Figure 4).

THE INTEGRATED MODEL OF IT PROJECT MANAGEMENTSYSTEM

The integrated model that was developed focuses on project planning and

control using the well-known concept of feedback controller (Yager and

Filew 1995). Fuzzy models were applied for control mechanism

description. Two mechanisms developed by Zeigler (1984) were used:

homogeneity, which is de®ned as having identical structures within a

block, and uniformity, which is de®ned as having identical effects for

superior and secondary elements. Also, the concepts of parallel and serial

decomposition with feedback were used (Szczerbicki and Orlowski in

press). As the result, to verify the integrated model its INPUT=OUTPUT

responses can be compared with the responses of the real life system.

Project platforms within the model are functional sections, developed

by following the descending systemic approach. The concept of platforms

allows for a multi-level decomposition of the organizationa l and IT

Figure 4. The state space concept applied in stage three of the IT project management model

development.

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infrastructure, design processes, and project management. The integrated

model developed allows for the distinction of any number of platforms

depending on the type of the IT project analyzed. Four different man-

agement spaces were proposed: team management, design management,

knowledge management, and supporting techniques management space.

Design management space consists of design processes. To de®ne design

processes a qualitative solution was developed using the phenomenologica l

approach to knowledge-based systems development. An attempt was made

to adopt the deductive approach based on Mesarovic and Takahara (1989).

Design synthesis and analysis both occur on parallel platforms.

A parallel design solution was also proposed for concurrent team and

project management allowing for independent and parallel indirect

acquisition of knowledge. The design and management processes are

conducted in a network software environment supported with selected IT

CASE tools.

In the knowledge management space the following takes place:

° Experts’ knowledge is used to de®ne the scope of the system, determine

its function, and the structure of the informal model;

° The experience of knowledge engineers is combined with that of ex-

perts creating a formal model to implement it as knowledge bases;

° The system designer’s tools are matched with design objects in areas

such as knowledge acquisition, decision-making support, and network

representation;

° IT solutions are developed for project management, design process,

and product evaluation.

The space of management support consists of knowledge codi®ed as

production rules in the following form:

IF uk is B10 AND u(k– 1) is B11 AND . . .AND y(k– n) is Aln THEN y(k) = Aln

(1)

where Aij; Bij are linguistic labels, uk-input values, and y(k) output values.

An example of the rule description is given in Figure 5.

FINE-TUNING OF THE INTEGRATED (FUZZY) MODEL

The process of ®ne-tuning of the fuzzy model consists of three stages. In

stage one the measurement data from the three research projects used in

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our case study were converted into production rules. Stage two was

concerned with building the membership function and stage three

involved model self-tuning and self-organizing.

The ®rst stage was basically a knowledge acquisition one. Building

the required knowledge base involved data gathering from three IT

research projects. The most important project in this case study was

ECOSIM (Ecological and Environmental Monitoring and Simulation

System for Management Decision Support in Urban Areas) which

involved 13 partners. The objective of the project was to develop an

environmental computer decision-support system working with interna-

tional monitoring networks and using simulation models of air, soil, and

water. The conceptual architecture of the project is depicted in Figure 6.

The above concept was the basis of the system development in ®re

steps as illustrated in Figure 7. Data from all stages were used in

knowledge acquisition and knowledge base development.

In the second stage membership functions were built using data on

management methods and IT tools gathered in stage one. Figure 8 shows

an example of how membership functions were computed.

The stage of self-tuning and self-organizing involves the following:

° addition of new rules,

° determination of data clusters,

Figure 5. Production rules used to describe the integrated model: an example.

MANAGING KNOWLEDGE-BASED IT PROJECTS 407

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° checking membership functions for the de®ned clusters,

° computation of the degree of rule con®rmation.

Model self-tuning was performed using measurement data on IT

project management in some additional projects taking part in the case

Figure 6. The concept of the ECOSIM system.

Figure 7. ECOSIM system development steps.

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studies that were performed. Fully developed and tuned integrated model

was veri®ed and validated using historical data from a number of soft-

ware development projects.

DISCUSSION REMARKS AND CONCLUSION

This paper signals a very important challenge in information society,

namely the problem of how to effectively manage and support the devel-

opment and implementation of a knowledge-based IT system. As some

contribution to the ways this challenge can be addressed, a system

approach was applied to the development of an IT project management

model. The modeling assumptions were provided by the use of deductive

and inductive approaches as well as deterministic and forecasting techni-

ques. The integrated model was implemented in knowledge-based system

development for the Environmental Department of Gdansk City Council,

Gdansk, Poland. Integration process involved design integration, man-

agement techniques, and knowledge source integration. Design processes

in the integrated model were represented by fuzzy techniques. Non-fuzzy

analytical techniques were used for management process representation.

The proposed approach to the problem of IT project management is

an example of a qualitative solution integrating methods and techniques

Figure 8. Computation of membership functions.

MANAGING KNOWLEDGE-BASED IT PROJECTS 409

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of management, IT technologies and techniques, management processes,

and knowledge processing for knowledge-based systems. The concept of

IT project management combines fuzzy analysis models with synthesis of

data and knowledge. As such it develops a useful framework of con-

trolled heuristic processes supported with algorithmic management pro-

cedures in a concurrent environment. Such a framework allows for a

comfortable project control increasing the prospects of timely and suc-

cessful project completion.

The proposed model creates a uniform environment for managing

a project team, design processes, and expert knowledge acquired in the

IT project infrastructure. It is de®ned as the environment of the operating

system, IT design tools, and tools that support the management processes.

The model solution de®nes the scopes and platforms of management

and integrates the processes involved in managing the project, the design

team, and knowledge and supporting techniques. It has the potential to

shorten the system’s development time and to increase its quality.

The integrated model can contribute to software engineering devel-

opment in two areas: as a qualitative tool providing a methodological

solution for managing IT projects for the class of knowledge-based sys-

tems, and as a model solution for designing IT tools of the CASE class

(Computer Aided Software Engineering) for managing an IT project.

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