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DSS and Expert Systems

Oracle Tips by Burleson Consulting

High Performance Data Warehousing

Decision Support Systems And Expert Systems  

Decision Support Systems And Data Warehouses

In order to be a DSS, a system must have the following characteristics:

* A nonrecurring problem needs to be solved. DSS technology is used primarily for novel and unique modeling situations that require the user to simulate the behavior of some real-world problem.

* Human input is required. A DSS makes decisions with users, unlike an expert system which makes decisions for users.

* A method is available for testing hypotheses. A true DSS allows the end user to develop models and simulate changes to the model. For example, the end user could ask questions like, “What will happen to my net return if I exchanged my IBM stock for Microsoft stock?” or “How much faster would I be able to service my customers if I add two more checkout registers?”

* Users must have knowledge of the problem being solved. Unlike an expert system that provides the user with answers to well-structured questions, decision support systems require the user to thoroughly understand the problem being solved. For example, a financial decision support system, such as the DSSF product, would require the user to understand the concept of a stock Beta. Beta is the term used to measure the covariance of an individual stock against the behavior of the market as a whole. Without an understanding of the concepts, a user would be unable to effectively use a decision support system.

* Ad hoc data queries are allowed. As users gather information for their decision, they make repeated requests to the online database, with one query answer stimulating another query. Because the purpose of ad hoc query is to allow free-form queries to decision information, response time is critical.

* More than one acceptable answer may be produced. Unlike an expert system, which usually produces a single, finite answer to a problem, a decision support system deals with problems that have a domain or range of acceptable solutions. For example, a user of DSSF may discover that many acceptable stock portfolios match the selection criteria of the user. Another good example is a manager who needs to place production machines onto an empty warehouse floor. The goal would be to maximize the throughput of work in process from raw materials to finished goods. Clearly, she could choose from a number of acceptable ways of placing the machines on the warehouse floor in order to achieve this goal. This is called the state space approach to problem-solving--first a solution domain is specified, then the user works to create models to achieve the desired goal state.

* External data sources are used. For example, a DSS may require classification of customers by Standard Industry Code (SIC) or customer addresses by Standard Metropolitan Statistical Area (SMSA). Many warehouse managers load this external data into the central warehouse.

This is an excerpt from "High Performance Data Warehousing", copyright 1997.

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