The DWS (Data Warehouse Striping) technique allows the distribution of large data warehouses through a cluster of computers. The data partitioning approach partition the facts tables through all nodes and replicates the dimension tables. The replication of the dimension tables creates a limitation to the applicability of the DWS technique to data warehouses with big dimensions. This paper proposes a strategy to handle large dimensions in a distributed DWS system and evaluates the proposed strategy experimentally. With the proposed strategy the performance speed up and scale up obtained in the DWS technique are not affected by the presence of big dimensions. Furthermore, it extends the scope of the technique to queries that browse big dimensions that can also benefit of the performance increase of the DWS technique.
Keywords
Data warehousing, distributed query execution
Subject
Data Warehousing
Related Project
DWS Data Warehouse Striping
Conference
ACM Seventh International Workshop on Data Warehousing and OLAP, DOLAP 2004, November 2004
Cited by
Year 2007 : 1 citations
C. D. de Aguiar Ciferri, R.R. Ciferri, D. T. Forlani, "Horizontal fragmentation as a technique to improve the performance of drill-down and roll-up queries?, Proceedings of the 2007 ACM symposium on Applied computing, Seoul, Korea 2007.
Year 2006 : 2 citations
Evelin Giuliana Lima, Marina Teresa Pires Vieira, "Ferramenta para Geração de Modelo Dimensional para Data Warehouses?, Simpósium Brasileiro de Banco de Dados, Florianópolis, SC, Brasil, 2006.
Diogo Tuler Forlani, Cristina Dutra de Aguiar Ciferri, Ricardo Rodrigues Ciferri, "Melhorando o Desempenho do Processamento de Consultas Drill-Across em Ambientes de Data Warehousing, Simpósium Brasileiro de Banco de Dados, Florianópolis, SC, Brasil, 2006.
