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Information Systems

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Information System (CS507)
LESSON 11
Data Mart
Data warehouses can become enormous with hundreds of gigabytes of transactions. As a result, subsets,
known as "data marts," are often created for just one department or product line. Data Warehouse
combines databases across an entire enterprise. However, Data Marts are usually smaller and focus on a
particular subject or department or product line.
Following are the common techniques through which a data warehouse can be used.
11.1 Online Analytical Processing (OLAP)
Decision support software that allows the user to quickly analyze information that has been
summarized into multidimensional views and hierarchies. The term online refers to the interactive
querying facility provided to the user to minimize response time. It enables users to drill down into
large volume of data in order to provide desired information, such as isolating the products that are
more volatile from sales data. OLAP summarizes transactions into multidimensional user defined views.
11.2 Data Mining
Data mining is also known as Knowledge-Discovery in Databases (KDD). Put simply it is the
processing of the data warehouse. It is a process of automatically searching large volumes of data for
patterns. The purpose is to uncover patterns and relationships contained within the business activity
and history and predict future behavior. Data mining has become an important part of customer
relationship management (CRM).
The data mining procedure involves following steps
Exploration ­ includes data preparation which may involve filtering data and data transformations,
selecting subsets of records.
Model building and validation ­ involves the use of various models for predictive performance (i.e.,
explaining the variability in question and producing stable results across samples). Each model
contains various patterns of queries used to discover new patterns and relations in the data.
Deployment ­ That final stage involves using the model selected as best in the previous stage and
applying it to new data in order to generate predictions or estimates of the expected outcome.
Example of Data Mining
Consider a retail sales department. Data mining system may infer from routine transactions that
customers take interests in buying trousers of a particular kind in a particular season. Hence, it can
make a correlation between the customer and his buying habits by using the frequency of his/her
purchases. The marketing department will look at this information and may forecast a possible clientele
for matching shirts. The sales department may start a departmental campaign to sell the shirts to buyers
of trousers through direct mail, electronic or otherwise. In this case, the data mining system generated
predictions or estimates about the customer that was previously unknown to the company.
Concept of Models Used in Decision Support System (DSS)
"A model is an abstract representation that illustrates the components or relationships of a
phenomenon."
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Information System (CS507)
Models are prepared so as to formulate ideas about the problem solutions that is allowing the managers to
evaluate alternative solutions available for a problem in hand.
11.3 Types of Models Used in DSS
Physical Models
Narrative Models
Graphic Models
Mathematical Models
11.3.1 Physical Models
Physical models are three dimensional representation of an entity (Object / Process). Physical models
used in the business world include scale models of shopping centres and prototypes of new
automobiles.
The physical model serves a purpose that cannot be fulfilled by the real thing, e.g. it is much less expensive
for shopping centre investors and automakers to make changes in the designs of their physical models
than to the final product themselves.
11.3.2 Narrative Models
The spoken and written description of an entity as Narrative model is used daily by managers and
surprisingly, these are seldom recognized as models.
For instance
All business communications are narrative models
11.3.3 Graphic Models
These models represent the entity in the form of graphs or pictorial presentations. It represents its entity
with an abstraction of lines, symbols or shapes. Graphic models are used in business to communicate
information. Many company's annual reports to their stockholders contain colourful graphs to convey
the financial condition of the firm.
For Instance
Bar graphs of frequently asked questions with number of times they are asked.
11.3.4 Mathematical Models
They represent Equations / Formulae representing relationship between two or more factors related to
each other in a defined manner.
Types of Mathematical Models
Mathematical models can further be classified as follows, based on
Influence of time ­ whether the event is time dependant or related
Degree of certainty ­ the probabilities of occurrence of an event
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Information System (CS507)
Level of optimization ­ the perfection in solution the model will achieve.
Hence use of right model in decision support software is critical to the proper functionality of the system.
Group DSS
When people responsible for decision making are geographically dispersed or are not available at a place at
the same time, GDSS is used for quick and efficient decision making. GDSS is characterized by being
used by a group of people at the same time to support decision making. People use a common
computer or network, and collaborate simultaneously.
An electronic meeting system (EMS) is a type of computer software that facilitates group decision-making
within an organization. The concept of EMS is quite similar to chat rooms, where both restricted or
unrestricted access can be provided to a user/member.
DSS vs. GDSS
DSS can be extended to become a GDSS through
 The addition of communication capabilities
 The ability to vote, rank, rate etc
 Greater system reliability
11.4 Knowledge / Intelligent Systems
Before we proceed with defining these systems, first we should have clear concept of Knowledge
Management. The set of processes developed in an organization to create, gather, store, maintain and
apply the firm's knowledge is called Knowledge Management. Hence the systems that aid in the
creation and integration of new knowledge in the organization are called knowledge systems.
There are two questions
Who are they built for?
This refers to defining the knowledge workers for whom the knowledge system is being built. The term
refers to people who design products and services and create knowledge for an organization. For instance
Engineers
Architects
Scientists
Knowledge systems are specially designed in assisting these professionals in managing the knowledge in
an organization.
What are they built for?
Every knowledge system is built to maintain a specific form of knowledge. Hence it needs to be defined
in the start, what the system would maintain. There are major types of knowledge.
Explicit knowledge ­ Structured internal knowledge e.g. product manuals, research reports, etc.
External knowledge of competitors, products and markets
Tacit knowledge ­ informal internal knowledge, which resides in the minds of the employees but
has not been documented in structured form.
Knowledge systems promote organizational learning by identifying, capturing and distributing these forms
of knowledge
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Information System (CS507)
11.5 Knowledge Support Systems (KSS) / Intelligent Systems
These systems are used to automate the decision making process, due to its high-level-problem-solving
support. KSS also has the ability to explain the line of reasoning in reaching a particular solution, which
DSS does not have.
Intelligent Systems
Knowledge systems are also called intelligent systems. The reason is that once knowledge system is up and
running, it can also enable non experts to perform tasks previously done by experts. This amounts to
automation of decision making process i.e. system runs independently of the person making decisions.
Artificial Intelligence
"Artificial intelligence is the ability of a machine to replicate the human thought processes. The way humans
proceed to analyze a problem and find appropriate solutions, similarly computers are geared up to follow
human logic to solve problems."
These knowledge-based applications of artificial intelligence have enhanced productivity in business,
science, engineering, and the military. With advances in the last decade, today's expert systems clients can
choose from dozens of commercial software packages with easy-to-use interfaces.
The most popular type of intelligent systems is the Expert System.
Expert System
An expert system is a computer program that attempts to represent the knowledge of human experts in
the form of Heuristics. It simulates the judgment and behaviour of a human or an organization that has
expert knowledge and experience in a particular field.
Examples are
 Medical diagnosis,
 Equipment repair,
 Investment analysis,
 Financial, estate and insurance planning,
 Vehicle routing,
 Contract bidding
Heuristics
Heuristic is the art and science of discovery and invention. The word comes from the same Greek root
as "eureka", which means "I have found it". A heuristic is a way of directing your attention fruitfully. It
relates to using a problem-solving technique, in which the most appropriate solution is found by
alternative methods. This solution is selected at successive stages of a program for use in the next step
of the program.
11.6 Components of an Expert System
There are four main components of Expert systems
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Information System (CS507)
User Interface: to enable the manager to enter instructions and information into an expert system to
receive information from it.
Knowledge Base: it is the database of the expert system. It contains rules to express the logic of the
problem.
Inference engine: it is the database management system of the expert system. It performs reasoning by
using the contents of the knowledge base.
Development engine ­ it is used to create an expert system.
Neural Network
Hardware or software that attempt to emulate the processing patterns of the biological brain. It is a device,
modeled after the human brain, in which several interconnected elements process information
simultaneously, adapting and learning from past patterns.
Neural Network vs. Expert System
Expert systems seek to model a human expert's way of solving problems. They are highly specific to seeking
solutions. Neural networks do not model human intelligence. They seek to put intelligence into the
hardware in the form of generalized capability to learn.
Fuzzy Logic
The word Fuzzy literally means vague, blurred, hazy, not clear. Real life problems may not be solved by an
optimized solution. Hence allowance needs to be made for any imperfections which may be faced while
finding a solution to a problem. Fuzzy logic is a form of algebra employing a range of values from "true" to
"false" that is used in decision-making with imprecise data, as in artificial intelligence systems. It is a rule
based technology that tolerates imprecision by using non specific terms/ imprecise concepts like "slightly",
"quite" and "very". to solve problems. It is based on the Possibility theory, which is a mathematical theory
for dealing with certain types of uncertainty and is an alternative to probability theory.
Executive Support Systems (ESS)
This Computer Based Information System (CBIS) is used by senior managers for strategic decision making.
The decisions at this level are non-routine and require judgment and evaluation. They draw summarized
information from internal MIS and Decision Support Systems. These systems deal with external influences
on an organization as well.
New Tax laws
Competitors
Acquisitions, take-overs, spin offs etc.
They filter, compress and track critical data so as to reduce time and effort required to obtain information
useful for executives. They are not designed to solve specific problems. They are generalized to be capable
of dealing with changing problems. Since executives have little contact with all levels of the organization,
ESS uses more graphical interface for quick decision making.
ESS vs. DSS
ESS implies more of a war room style graphical interface that overlooks the entire enterprise. A decision
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Table of Contents:
  1. Need for information, Sources of Information: Primary, Secondary, Tertiary Sources
  2. Data vs. Information, Information Quality Checklist
  3. Size of the Organization and Information Requirements
  4. Hierarchical organization, Organizational Structure, Culture of the Organization
  5. Elements of Environment: Legal, Economic, Social, Technological, Corporate social responsibility, Ethics
  6. Manual Vs Computerised Information Systems, Emerging Digital Firms
  7. Open-Loop System, Closed Loop System, Open Systems, Closed Systems, Level of Planning
  8. Components of a system, Types of Systems, Attributes of an IS/CBIS
  9. Infrastructure: Transaction Processing System, Management Information System
  10. Support Systems: Office Automation Systems, Decision Support Systems, Types of DSS
  11. Data Mart: Online Analytical Processing (OLAP), Types of Models Used in DSS
  12. Organizational Information Systems, Marketing Information Systems, Key CRM Tasks
  13. Manufacturing Information System, Inventory Sub System, Production Sub System, Quality Sub system
  14. Accounting & Financial Information Systems, Human Resource Information Systems
  15. Decision Making: Types of Problems, Type of Decisions
  16. Phases of decision-making: Intelligence Phase, Design Phase, Choice Phase, Implementation Phase
  17. Planning for System Development: Models Used for and Types of System Development Life-Cycle
  18. Project lifecycle vs. SDLC, Costs of Proposed System, Classic lifecycle Model
  19. Entity Relationship Diagram (ERD), Design of the information flow, data base, User Interface
  20. Incremental Model: Evaluation, Incremental vs. Iterative
  21. Spiral Model: Determine Objectives, Alternatives and Constraints, Prototyping
  22. System Analysis: Systems Analyst, System Design, Designing user interface
  23. System Analysis & Design Methods, Structured Analysis and Design, Flow Chart
  24. Symbols used for flow charts: Good Practices, Data Flow Diagram
  25. Rules for DFDs: Entity Relationship Diagram
  26. Symbols: Object-Orientation, Object Oriented Analysis
  27. Object Oriented Analysis and Design: Object, Classes, Inheritance, Encapsulation, Polymorphism
  28. Critical Success Factors (CSF): CSF vs. Key Performance Indicator, Centralized vs. Distributed Processing
  29. Security of Information System: Security Issues, Objective, Scope, Policy, Program
  30. Threat Identification: Types of Threats, Control Analysis, Impact analysis, Occurrence of threat
  31. Control Adjustment: cost effective Security, Roles & Responsibility, Report Preparation
  32. Physical vs. Logical access, Viruses, Sources of Transmissions, Technical controls
  33. Antivirus software: Scanners, Active monitors, Behavior blockers, Logical intrusion, Best Password practices, Firewall
  34. Types of Controls: Access Controls, Cryptography, Biometrics
  35. Audit trails and logs: Audit trails and types of errors, IS audit, Parameters of IS audit
  36. Risk Management: Phases, focal Point, System Characterization, Vulnerability Assessment
  37. Control Analysis: Likelihood Determination, Impact Analysis, Risk Determination, Results Documentation
  38. Risk Management: Business Continuity Planning, Components, Phases of BCP, Business Impact Analysis (BIA)
  39. Web Security: Passive attacks, Active Attacks, Methods to avoid internet attacks
  40. Internet Security Controls, Firewall Security SystemsIntrusion Detection Systems, Components of IDS, Digital Certificates
  41. Commerce vs. E-Business, Business to Consumer (B2C), Electronic Data Interchange (EDI), E-Government
  42. Supply Chain Management: Integrating systems, Methods, Using SCM Software
  43. Using ERP Software, Evolution of ERP, Business Objectives and IT
  44. ERP & E-commerce, ERP & CRM, ERP Ownership and sponsor ship
  45. Ethics in IS: Threats to Privacy, Electronic Surveillance, Data Profiling, TRIPS, Workplace Monitoring