Exam 2 Review October 30, 2014 Slide 2 Info. Systems in
Organizations Decision Making Slide 3 3 IS & Hierarchical
Organizational structure. Slide 4 4 Administrative Information
Systems Transaction Processing Systems (TPS) Basic business system
that serves the operational level (including analysts) in
organizations Capture & process data generated during
day-to-day activities Office Automation Systems (OAS) Systems
designed to help office workers in doing their job. Decision
Support Systems (DSS) Systems designed to support middle managers
and business professionals during the decision-making process
Executive Information Systems (EIS) or Executive Support Systems
(ESS) Specialized DSS that help senior level executives make
decisions. GDSS: computer-based systems that facilitate solving of
unstructured problems by set of decision makers Slide 5 5
Organization & IS: another view Top Management Middle
Management Lower Management Operational workers Office workers
Knowledge workers Types of Information Systems: - Transaction
Processing Systems - Office Automation Systems - Knowledge Worker
Systems - Management Information Systems - Decision Support Systems
- Executive Information Systems Q: What kind of IS are designed to
provide help for decision makers? Questions Slide 6 6 Decision
Making process Simons decision-making process model Intelligence
Design Choice (Implementation) Newell, A., and Simon, H. A. (1972).
Human problem solving Englewood Cliffs, Prentice-Hall, New Jersey.
Herbert Simon (1955), A Behavioral Model of Rational Choice,
Quarterly Journal of Economics, vol. 69, 99188 Slide 7 7
Intelligence Phase Scan the environment for a problem. Determine if
decision- maker can solve the problem. Within their scope of
influence? Fully define the problem by gathering more information
about the problem. Scan Environment for problem to be solved or
decision to be made Data source Organizational IS Problem ? END
Problem within scope of influence? No Yes END No Gather more
information about the problem Internal & External data Yes
Slide 8 8 Design Phase Develop a model of the problem. Determine
type of model. Verify model. Develop and analyze potential
solutions. Develop a model of problem to be solved Verify that the
model is accurate Develop potential solutions Slide 9 9 Choice
Phase Select the solution to implement. More detailed analysis of
selected solutions might be needed. Verify initial conditions.
Analyze proposed solution against real-world constraints. Questions
Slide 10 10 DSS structure Systems designed to help middle managers
make decisions Major components Data management subsystem Internal
and external data sources Analysis subsystem Typically mathematical
in nature User interface How the people interact with the DSS Data
visualization is the key Text Graphs Charts User Interface Analysis
- Sensitivity Analysis - What-if Analysis - Goal-seeking Analysis
-Data-driven tools -> Data mining -> OLAP* Data Management -
Transactional Data - Data warehouse - Business partners data -
Economic data * OLAP: OnLine Analytical Processing Slide 11 11 DSS
Analysis Tools Simulation is used to examine proposed solutions and
their impact Sensitivity analysis Determine how changes in one part
of the model influence other parts of the model What-if analysis
Manipulate variables to see what would happen in given scenarios
Goal-seeking analysis Work backward from desired outcome Determine
monthly payment given various interest rates. Works backward from a
given monthly payment to determine various loans that would give
that payment. Slide 12 12 Executive Information Systems Specialized
DSS that supports senior level executives within the organization
Most EISs offer the following capabilities: Consolidation involves
the aggregation of information and features simple roll-ups to
complex groupings of interrelated information Drill-down enables
users to get details, and details of details, of information
Slice-and-dice looks at information from different perspectives
Digital dashboards are common features Slide 13 13 Artificial
Intelligence (AI) systems Common categories of AI systems: 1.Expert
system computerized advisory programs that imitate the reasoning
processes of experts in solving difficult problems 2.Neural Network
attempts to emulate the way the human brain works Analyses large
quantities of info to establish patterns and characteristics in
situations where logic or rules are unknown Uses Fuzzy logic a
mathematical method of handling imprecise or subjective information
3.Genetic algorithm an artificial intelligent system that mimics
the evolutionary, survival-of-the-fittest process to generate
increasingly better solutions to a problem 4.Intelligent agent
special-purposed knowledge-based information system that
accomplishes specific tasks on behalf of its users Slide 14 14
Expert Systems Artificial Intelligence systems that codify human
expertise in a computer system Main goal is to transfer knowledge
from one person to another Wide range of subject areas Medical
diagnosis Computer purchasing Knowledge engineer elicits the
expertise from the expert and encodes it in the expert system Slide
15 15 Expert Systems Components Knowledge base: database of the
expertise, often in IF THEN rules. Inference engine: derives
recommendations from knowledge base and problem-specific data User
interface: controls the dialog between the user and the system
Explanation system: Explain the how and why of recommendations
Knowledge base Domain Expert Knowledge Engineer Expertise
Explanation System Inference Engine User Interface User System
Engineer Encoded expertise IF family is albatross AND color is
white THEN bird is laysan albatross. IF family is albatross AND
color is dark THEN bird is black footed albatross Example of rules
- Knowledge engineer codify the human experts expertise into the
systems knowledge base. - System engineer is the IT professional
who develop the user interface, the inference engine, and the
explanation system. Slide 16 Database & Data Warehouse Slide 17
17 Basic Concepts of Database systems Table Two-dimensional
structure composed of rows and columns Field Like a column in a
spreadsheet Field name Like a column name in a spreadsheet
Examples: AccountID, Customer, Type, Balance Field values Actual
data for the field Record Set of fields that describe an entity (a
person, an account, etc.) Primary key A field, or group of fields,
that uniquely identifies a record AccountIDCustomerTypeBalance
660001John SmithChecking$120.00 660002Linda MartinSaving$9450.00
660003Paul GrahamChecking$3400.00 Accounts table Each table has:
Fields Records 1 Primary key Slide 18 18 Basic Concepts in Data
Management A Primary key could be a single field like in these
tables AccountIDCustomerTypeBalance 660001John SmithChecking$120.00
660002Linda MartinSaving$9450.00 660003Paul GrahamChecking$3400.00
Primary key Primary key could be a composite key, i.e. multiple
fields Slide 19 19 Traditional File Systems System of files that
store groups of records used by a particular software application
Simple but with a cost Inability to share data Inadequate security
Difficulties in maintenance and expansion Allows data duplication
(e.g. redundancy) Application 1 Program 1 File 1 File 2 File 3
Program 2 File 1 File 2 File 3 Application 2 Program 1 File 1 File
2 File 3 Program 2 File 1 File 2 File 3 Slide 20 20 Traditional
File System Anomalies Insertion anomaly Data needs to be entered
more than once if located in multiple file systems Modification
anomaly Redundant data in separate file systems Inconsistent data
in your system Deletion anomaly Failure to simultaneously delete
all copies of redundant data Deletion of critical data Slide 21
Database Advantages Database advantages from a business perspective
include Ease of data insertion Example: can insert a new address
once; and the address is updated in all forms, reports, etc.
Increased flexibility Handling changes quickly and easily Increased
scalability and performance Scalability: how the DB can adapt to
increased demand Reduced information redundancy & inconsistency
Increased information integrity (quality) Cant delete a record if
related info is used in other container Increased information
security Slide 22 22 Types of DBMSs Desktop Designed to run on
desktop computers Used by individuals or small businesses Requires
little or no formal training Does not have all the capabilities of
larger DBMSs Examples: Microsoft Access, FileMaker Desktop Server /
Enterprise Handheld Slide 23 23 Types of DBMSs (Cont.) Server /
Enterprise Designed for managing larger and complex databases by
large organizations Typically operate in a client/server setup
Either centralized or distributed Centralized all data on one
server Easy to maintain Prone to run slowly when many simultaneous
users No access if the one server goes down Distributed each
location has part of the database Very complex database
administration Usually faster than centralized If one server
crashes, others can still continue to operate. Examples: Oracle
Enterprise, DB2, Microsoft SQL Server Slide 24 24 Types of DBMSs
(Cont.) Handheld Designed to run on handheld devices Less complex
and have less capabilities than Desktop or Server DBMSs Example:
Oracle Database Lite, IBMs DB2 Everywhere. Slide 25 25 DBMS
Functions Create database structure (tables, relationships, schema,
etc.) Transform data into information (reports,..) Provide user
with different logical views of actual database content Provide
security: password authentication, access control DBMSs control who
can add, view, change, or delete data in the database ID Name Amt
01 John 23.00 02 Linda 3.00 03 Paul 53.00 Physical view ID Name 02
Linda Name Amt Paul 53.00 ID Name Amt 01 John 23.00 02 Linda 3.00
Logical views Slide 26 26 DBMS Functions (cont.) Allowing
multi-user access with control Control concurrency of access to
data Prevent one user from accessing data that has not been
completely updated When selling tickets online, Ticketmaster allows
you to hold a ticket for only 2 minutes to make your purchase
decision, then the ticket is released to sell to someone else that
is concurrency control Slide 27 27 Database Models Database model =
a representation of the relationship between structures (e.g.
tables) in a database Common database models Flat file model
Relational model (the most common, today) Object-oriented database
model Slide 28 28 Flat File Database model Stores data in basic
table structures No relationship between tables Used on PDAs for
address book Slide 29 29 Relational Database Model Multiple
two-dimensional tables related by common fields Uses controlled
redundancy to create fields that provide linkage relationships
between tables in the database These fields are called foreign keys
the secret to a relational database A foreign key is a field, or
group of fields, in one table that is the primary key of another
table Handles One-to-Many and One-to-One relationships Slide 30 30
Object-Oriented Database model Needed for multimedia applications
that manage images, voice, videos, graphics, etc. Used in
conjunction with Object-oriented programming languages Slower
compared to relational DBMS for processing large volume of
transactions Hybrid object-relational Databases are emerging Slide
31 Data Warehouse A logical collection of information gathered from
many different operational databases Supports business analysis
activities and decision-making tasks The primary purpose of a data
warehouse is to aggregate information throughout an organization
into a single repository for decision-making purposes 31 Slide 32
32 Data Warehouse Fundamentals Many organizations need internal,
external, current, and historical data Data Warehouse are designed
to, typically, store and manage data from operational transaction
systems, Web site transactions, external sources, etc. Slide 33
Multidimensional Analysis Data mining the process of analyzing data
to extract information not offered by the raw data alone
Data-mining tools use a variety of techniques (fuzzy- logic, neural
networks, intelligent agents) in order to find patterns and
relationships in large volumes of data and infer rules that predict
future behavior and guide decision making Other analytical tools:
query tools, statistical tools, etc. used to Analyze data,
determine relationships, and test hypotheses about the data 33
Slide 34 Data Warehouse Fundamentals Extraction, transformation,
and loading (ETL) a process that extracts information from internal
and external databases, transforms the information using a common
set of enterprise definitions, and loads the information into a
data warehouse. Slide 35 Information Cleansing or Scrubbing
Organizations must maintain high-quality data in the data warehouse
Information cleansing or scrubbing a process that weeds out and
fixes or discards inconsistent, incorrect, or incomplete
information first, occurs during ETL. Then, when the data is in the
Data Warehouse using Information cleansing or scrubbing tools. 35
Slide 36 36 Data Mart Subset of data warehouses that is highly
focused and isolated for a specific population of users Example:
Marketing data mart, Sales data mart, etc. Slide 37 Database vs.
Data Warehouse Databases contain information in a series of
two-dimensional tables In a Data Warehouse and data mart,
information is multidimensional, it contains layers of columns and
rows 37 Date Product Country sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr
U.S.A Canada Mexico sum Total annual sales of TV in U.S.A. Slide 38
Networking & Telecom Slide 39 Why Networking ? Resource sharing
Sharing hardware (printers, processors, etc.) Sharing software
(programs, data files) High reliability Can set automatic backup of
programs and data at different locations Fault tolerance (if one
server is down, others can provide service. If a disk fails, data
available through mirror or RAID-3 disks) Possible cost savings
Communication tool Internal email service Remote Access service 39
Slide 40 Computer Network An interconnection of computers and
computing equipment using either wires or wireless transmission
media over small or large geographical distances. Connect to GHI
ABC DEF GHI JKL MNO Once connected to the network, the computer (or
another device) becomes a network node 40 Slide 41 Network scope
Local area network (LAN): computer network where the nodes are all
in close proximity spanning a room, building, or campus
Metropolitan area network (MAN): network that serves an area of 3
to 30 miles - approximately the area of a typical city. Wide area
network (WAN): a large network that encompasses parts of states,
multiple states, countries, and the world 41 Slide 42 Transmission
Media Physical media Transmission media used to physically connect
nodes to the network Transmits electrical or optical signals Could
be copper wire or fiber optic cable Physical Wireless 42 Slide 43
Transmission Media (Continued) Twisted Pair CategoryUseSignalData
rateDistanceProblem Category 1TelephoneAnalog/Digital
