Multidimensional Partitioning of a Data Warehouse Star Schema

S. Mukherjee, H. Sharda, and D. Taniar (Australia)


datawarehouse, data partitioning, star schema, Cognos


A data warehouse is a repository of information integrated from multiple information sources that are used primarily for complex querying, analysis and decision support purposes. As decision support applications often require minimum response times to answer complex ad-hoc queries over vast repositories of information, efficient query processing is a critical requirement for data warehousing systems. Parallel systems and different partitioning techniques improve the operation and processing speed of a decision support system thereby reducing the time required to retrieve relations. This paper presents different partitioning approaches in a general framework that can be best implemented in different schemas used in data warehouse environments, resulting in optimisation of query response time. We have proposed different multidimensional partitioning models and compared the advantages and disadvantages of the proposed models. In this research, we have tested the multidimensional partitioning models in a product distribution data warehouse environment, which reveals ease of understanding of the models and the efficiency in representing complex information.

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