文章基本信息

标题：A Compacted Bitmap Vector Technique to Evaluate Iceberg Queries Efficiently
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作者：K.Sunil Kumar ; M.Laxmaiah ; Dr. C.Sunil Kumar 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：6
出版社：S.S. Mishra
摘要：the data storage and retrieving plays vital role in the data clustering (DC) and data warehousing (DW) procedures. The efficiency of a data retrieving technique depends on specific queries for retrieving the data from the relational database. Iceberg (IB) query is a distinctive class of aggregation query, which computes aggregate values beyond a given threshold (TH). Many data mining (DM) queries are mostly IB queries. The major part taken into the kindness about the AND operation in the IB queries. The condensed number of AND operation increases the efficiency of the IB query. In this effort, a well-organized IB query evaluation process is proposed by reducing the bitwise AND operations needed to find the item pairs (IPs). In the proposed scheme two keys are introduced to reduce the bitwise AND operations. Arbitrarily identifying 'N' 1-bit positions instead of first 1-bit position and dipping the zero bit values from the Most Significant (MS) Side so that the bit map vector (BMV) will be condensed so that, the bitwise operations needed is reduced. The testing is conducted on two datasets (DSs) in order to assess the performance of the proposed IB query evaluation algorithm. In the case of retail datasets, the time required for processing 120000 tuples is 857 milliseconds
关键词：Database; iceberg (IB) query; bitwise-AND; dynamic pruning; bitmap vector