Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
Han, J., Stefanovic, N., Koperski, K.: Selective Materialization: An Efficient Method for Spatial Data Cube Construction. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, Springer, Heidelberg (1998)
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)
Moore, D.: Fast hilbert curve generation, sorting, and range queries, http://www.caam.rice.edu/~dougm/twiddle/Hilbert
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)
Zeng, Z., Bharadwaj, V.: Design and analysis of a non-preemptive decentralized load balancing algorithm for multi-class jobs in distributed networks. Computer Communications 27, 679–694 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gorawski, M., Chechelski, R. (2006). Online Balancing of aR-Tree Indexed Distributed Spatial Data Warehouse. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_57
Download citation
DOI: https://doi.org/10.1007/11752578_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34141-3
Online ISBN: 978-3-540-34142-0
eBook Packages: Computer ScienceComputer Science (R0)