CS507 - Information Systems - Lecture Handout 11

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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.

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.

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.”

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.

Types of Models Used in DSS

  • Physical Models
  • Narrative Models
  • Graphic Models
  • Mathematical Models

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.

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

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.

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
  • 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 can be extended to become a GDSS through

  • The addition of communication capabilities
  • The ability to vote, rank, rate etc
  • Greater system reliability

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

  • 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

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


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.

Components of an Expert System

There are four main components of Expert systems

  • 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 implies more of a war room style graphical interface that overlooks the entire enterprise. A decision support system (DSS) typically provides a spreadsheet style "what if?" analysis capability, often for only one department or one product at time.