Computer simulation is one area that is used heavily within the modeling components of decision support systems. In fact, one of the first object-oriented languages was SIMULA, which was used as a driver for these what-if scenarios, and was incorporated into decision support systems so that users could model a particular situation. The user would create a scenario with objects that were subjected to a set of predefined behaviors.

The general characteristics of a decision support system include:

•  A nonrecurring problem 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.

•  A DSS requires human intuition—DSS makes the decision with the user, unlike expert systems that make the decision for the user.

•  A DSS requires 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 utilize a decision support system.

•  A DSS allows ad hoc data query—As users gather information for their decision, they make repeated requests to the online database, with one query answer stimulating another query. Since the purpose of ad hoc query is to allow free-form query to decision information, response time is critical.

•  Decision support systems may produce more than one acceptable answer—Unlike an expert system that 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 many “acceptable” stock portfolios that 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, he 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.

•  A DSS uses external data sources—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.

Decision support systems also allow the user to create what if scenarios. These futuristic glimpses are essentially modeling tools that allow the user to define an environment and simulate the behavior of that environment under changing conditions. For example, the user of a DSS for finance could create a hypothetical stock portfolio and then direct the DSS to model the behavior of that stock portfolio under different market conditions. Once these behaviors are specified, the user may vary the contents of the portfolio and view the results.

The type of output from decision support systems include:

•  Management Information Systems (MIS)—Standard reports and forecasts of sales.

•  Hypothesis testing—Did sales decrease in the eastern region last month because of changes in buying habits? This involves iterative questioning, with one answer leading to another question.

•  Model building—Creating a sales model and validating its behavior against the historical data in the warehouse. Predictive modeling is often used to forecast behaviors based upon historical factors.

•  Discovery of unknown trends—For example, why are sales up in the eastern region? Data mining tools answer questions in those instances where you may not even know what specific questions to ask.

The role of human intuition in this type of problem solving has stirred great debate. Decision support systems allow the user to control the decision-making process, applying his or her own decision-making rules and intuition to the process. However, the arguments for and against using artificial intelligence to manage the intuitive component of these systems has strong proponents on both sides.

Data Warehouses

Multidimensional databases are approaching the DSS market through two methods. The first approach is though “niche” servers that use a proprietary architecture to model multidimensional databases. Examples of niche servers include Arbor and IRI. The second approach is to provide multidimensional front ends that manage the mapping between the RDBMS and the dimensional representation of the data. Figure 10.3 offers an overview of the various multidimensional databases.

Figure 10.3  The major types of multidimensional databases.

In general, the following definitions apply to data warehouses:

•  Subject oriented data—Unlike an online transaction processing application that is focused on a finite business transaction, a data warehouse attempts to collect all that is known about a subject area (e.g., sales volume, interest earned) from all data sources within the organization.

•  Read-only during queries—Data warehouses are loaded during off hours and are used for read-only requests during day hours.

•  Highly denormalized data structures—Unlike an OLTP system with many “narrow” tables, data warehouses prejoin tables, creating “fat” tables with highly redundant columns.

•  Data is preaggregated—Unlike OLTP, data warehouses precalculate totals to improve runtime performance. Note that preaggregation is anti-relational, meaning that the relational model advocates building aggregate object at runtime, only allowing for the storing of atomic data components.

•  Features interactive, ad hoc query—Data warehouses must be flexible enough to handle spontaneous queries by the users. Consequently, a flexible design is imperative.