Generic data model pattern for data warehouse

Useful decision-making information can be proceed through a subject-oriented data warehouse in which it will store an integrated, time-variant, and non volatile collected data. The key to find such data warehouse is to have a good data model that defines the structure of data kept in the data warehouse. Actually the quality of correctness and completeness of an information depends on how well the data model is constructed. One way to get a good data model is by utilizing patterns. This research derived eighteen patterns of generic data model of a warehouse which can be used and chosen. They are created based on analysis of data warehousing needs, existing patterns, and Kimball's case studies. To measure the level of reusability of the patterns four metrics are defined. Two metrics related to flexibility and two metrics related to comprehensibility. The test result on the pattern reusability shows that the flexibility metrics score are adequate, while the comprehensibility metrics score are almost perfect. The patterns occur in different frequencies test has involving two case studies. It concluded that patterns which are associated with the changes in dimensions, product heterogeneity and multi valued attributes are seldom or almost never used. Further patterns that are used frequently are patterns related with dimension tables, especially generic dimension pattern and date pattern.