Abstract
Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. So we propose two novel clustering methods for query optimization: the recursion clustering method for point queries and the hierarchical clustering method for range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.
Supported by the National Natural Science Foundation of China under Grant No.60473073, 60573090, 60673139.
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© 2007 Springer-Verlag Berlin Heidelberg
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Leng, F., Bao, Y., Wang, D., Yu, G. (2007). A Clustered Dwarf Structure to Speed Up Queries on Data Cubes. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_16
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DOI: https://doi.org/10.1007/978-3-540-74553-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74552-5
Online ISBN: 978-3-540-74553-2
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