Decision-support database applications are optimal for data queries that do not change data. For example, a company can periodically summarize its sales data by date, sales region, or product, and store this information in a separate database to be used for analysis by senior management. To make business decisions, users must be able to determine trends in sales quickly by querying the data based on various criteria. However, they do not have to change this data. The tables in a decision-support database are heavily indexed, and the raw data is frequently preprocessed and organized to support the various types of queries to be used. Because the users are not changing data, concurrency and atomicity issues are not a concern; the data is changed only by periodic, bulk updates made during off-hour, low-traffic times in the database.
Decision Support Design Considerations
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Decision-support system databases should be designed to promote the following:
- Heavy indexing
Decision-support systems have low update requirements, but large volumes of data. Use many indexes to improve query performance.
- Denormalization of the database
Introduce pre-aggregated or summarized data to satisfy common query requirements and improve query response times.
- Use of a star or snowflake schema to organize the data within the database.
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