Online Balancing of aR-Tree Indexed Distributed Spatial Data Warehouse
One of the key requirements of data warehouses is query response time. Amongst all methods of improving query performance, parallel processing (especially in shared nothing class) is one of the giving practically unlimited system’s scaling possibility. The complexity of data warehouse systems is very high with respect to system structure, data model and many mechanisms used, which have a strong influence on the overall performance. The main problem in a parallel data warehouse balancing is data allocation between system nodes. The problem is growing when nodes have different computational characteristics. In this paper we present an algorithm of balancing distributed data warehouse built on shared nothing architecture. Balancing is realized by iterative setting dataset size stored in each node. We employ some well known data allocation schemes using space filling curves: Hilbert and Peano. We provide a collection of system tests results and its analysis that confirm the possibility of a balancing algorithm realization in a proposed way.
KeywordsData Warehouse Range Query Query Execution Fact Table System Node
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- 1.Bernardino, J., Madeira, H.: Data Warehousing and OLAP: Improving Query Performance Using Distributed Computing. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, Springer, Heidelberg (2000)Google Scholar
- 2.Dehne, F., Eavis, T., Rau-Chaplin, A.: Parallel Multi-Dimensional ROLAP Indexing. In: 3rd International Symposium on Cluster Computing and the Grid, Tokyo, Japan (2003)Google Scholar
- 3.Faloutsos, C., Bhagwat, P.: Declustering using fractals. In: Proc. of the Int’l Conf. on Parallel and Distributed Information Systems, San Diego, California, January 1993, pp. 18–25 (1993)Google Scholar
- 4.Faloutsos, C., Roseman, S.: Fractals for Secondary Key Retrieval. Technical Report UMIACS-TR-89-47, CS-TR-2242, University of Maryland, Colledge Park, Maryland (May 1989)Google Scholar
- 5.Gorawski, M., Malczok, R.: Distributed Spatial Data Warehouse Indexed with Virtual Memory Aggregation Tree. In: 5th Workshop on Spatial-Temporal DataBase Management (STDBM_VLDB 2004), Toronto, Canada (2004)Google Scholar
- 7.Hua, K., Lo, Y., Young, H.: GeMDA: A Multidimensional Data Partitioning Technique for Multiprocessor Database Systems. Distributed and Parallel Databases, University of Florida, 9, 211–236 (2001)Google Scholar
- 8.Moore, D.: Fast hilbert curve generation, sorting, and range queries, http://www.caam.rice.edu/~dougm/twiddle/Hilbert
- 9.Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP Operations in Spatial Data Warehouses. In: Agha, G.A., De Cindio, F., Rozenberg, G. (eds.) APN 2001. LNCS, vol. 2001, Springer, Heidelberg (2001)Google Scholar