Cited By
View all- Wang RWan DZhang ZZhao Q(2019)Research on Parallel Computing Method of Hydrological Data CubeBig Data – BigData 201910.1007/978-3-030-23551-2_4(49-64)Online publication date: 25-Jun-2019
A Clustered Dwarf Structure to Speed Up Queries on Data Cubes
Authors: 
Fangling Leng, Yubin Bao, Daling Wang, Ge YuAuthors Info & Claims
Fangling Leng, Yubin Bao, Daling Wang, Ge YuAuthors Info & ClaimsPages 170 - 180
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.
- A Clustered Dwarf Structure to Speed Up Queries on Data Cubes
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Copyright © Springer-Verlag Berlin Heidelberg.
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Springer-Verlag
Berlin, Heidelberg
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Published: 03 September 2007
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View all- Wang RWan DZhang ZZhao Q(2019)Research on Parallel Computing Method of Hydrological Data CubeBig Data – BigData 201910.1007/978-3-030-23551-2_4(49-64)Online publication date: 25-Jun-2019