What can partitioning do for your data warehouses and data marts?

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 warehous...

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Bibliographic Details
Main Authors: Bellatreche, L., Karlapalem, K., Mohania, M., Schneider, M.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Application software
Computer science
Costs
Data warehouses
Delay
Distributed databases
Greedy algorithms
Query processing
Warehousing
Online Access:Request full text
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Summary: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.
DOI:10.1109/IDEAS.2000.880634