Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
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Communications of the ACM
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Information Systems
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IEEE Transactions on Software Engineering
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ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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The 3rd international workshop on self-managing database systems (SMDB'08)
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
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HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Proceedings of the 17th International Database Engineering & Applications Symposium
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One of the most important metrics in measuring the performance of a database system is query response time, which is composed of I/O time and CPU time. I/O time is decided by the amount of data read/write from/to disks and how the data is located on disks. CPU time is decided by how the database system performs the query operations. So if we want to reduce the query response time we can reduce either I/O time or CPU time, or both of them. We know retrieving data from disks is much slower than retrieving data from main memory. Hence, one of the common ways to reduce I/O times is clustering data on disks so that queries will access only relevant data. This paper introduces an efficient algorithm, called AutoClust, for automatic database attribute clustering (or also called automatic database vertical partitioning) for single computers as well as cluster computers. It is based on closed item sets mined from queries and their attributes using association rule mining. The paper then presents experimental results comparing the performance of AutoClust with that of a baseline algorithm on both single computers and cluster computers using the TPC-H benchmark running on major commercial database systems. The experiments show that AutoClust has better query costs for both types of computers.