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http://dx.doi.org/10.3745/KIPSTD.2012.19D.1.001

A Study on Selecting Bitmap Join Index to Speed up Complex Queries in Relational Data Warehouses  

An, Hyoung-Geun (울산대학교 전기공학부)
Koh, Jae-Jin (울산대학교 전기공학부)
Publication Information
The KIPS Transactions:PartD / v.19D, no.1, 2012 , pp. 1-14 More about this Journal
Abstract
As the size of the data warehouse is large, the selection of indices on the data warehouse affects the efficiency of the query processing of the data warehouse. Indices induce the lower query processing cost, but they occupy the large storage areas and induce the index maintenance cost which are accompanied by database updates. The bitmap join indices are well applied when we optimize the star join queries which join a fact table and many dimension tables and the selection on dimension tables in data warehouses. Though the bitmap join indices with the binary representations induce the lower storage cost, the task to select the indexing attributes among the huge candidate attributes which are generated is difficult. The processes of index selection are to reduce the number of candidate attributes to be indexed and then select the indexing attributes. In this paper on bitmap join index selection problem we reduce the number of candidate attributes by the data mining techniques. Compared to the existing techniques which reduce the number of candidate attributes by the frequencies of attributes we consider the frequencies of attributes and the size of dimension tables and the size of the tuples of the dimension tables and the page size of disk. We use the mining of the frequent itemsets as mining techniques and reduce the great number of candidate attributes. We make the bitmap join indices which have the least costs and the least storage area adapted to storage constraints by using the cost functions applied to the bitmap join indices of the candidate attributes. We compare the existing techniques and ours and analyze them in order to evaluate the efficiencies of ours.
Keywords
Bitmap Join Index; Frequent ItemSets; Data Mining; Relational Data Warehouse;
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