PartJoin: An Efficient Storage and Query Execution for Data Warehouses
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Some issues in design of data warehousing systems
Data warehousing and web engineering
Foundations and Trends in Databases
Partitioning methods for multi-version XML data warehouses
Distributed and Parallel Databases
Squash: A Tool for Analyzing, Tuning and Refactoring Relational Database Applications
Applications of Declarative Programming and Knowledge Management
An evolutionary approach to schema partitioning selection in a data warehouse
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multi-ways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting "best" dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.