CS507

Information Systems

Lesson # 11

Online Analytical Processing

Online Analytical Processing• Introduction• Online Analytical Processing • Data Mining• Models Used in Decision Support System• Mathematical Models• Knowledge / Intelligent Systems• Knowledge Support Systems (KSS) / Intelligent

Systems• Expert System• Executive Support Systems (ESS)

Introduction

• 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.

Online Analytical Processing (OLAP)

• 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). 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 (Continued)• 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.

Models Used in Decision Support System

• 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 like object or process.  Physical models used in the business world include scale models of shopping centres and prototypes of new automobiles.

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.

• 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 colorful graphs to convey the financial condition of the firm. 

Mathematical Models

• They represent Equations or Formulae representing relationship between two or more factors related to each other in a defined manner.

• Mathematical models can further be classified as follows, based on:– Influence of time – Degree of certainty – Level of optimization

Knowledge / Intelligent Systems

• Knowledge systems are specially designed in assisting these professionals in managing the knowledge in an organization.

• 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. 

Knowledge / Intelligent Systems (Continued)

• 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.

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. For example medical diagnosis, equipment repair, Investment analysis etc.

Components of an Expert System

• 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

Executive Support Systems (ESS)

• ESS implies more of a war room style graphical interface that overlooks the entire enterprise. It uses more graphical interface for quick decision making.