Vertical Fragmentation in Databases Using Data-Mining Technique

Narasimhaiah Gorla; Pang W.Y. Betty

doi:10.4018/jdwm.2008070103

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Vertical Fragmentation in Databases Using Data-Mining Technique

Narasimhaiah Gorla (American University of Sharjah, UAE) and Pang W.Y. Betty (Hong Kong Polytechnic University, Hong Kong)

Source Title: International Journal of Data Warehousing and Mining (IJDWM) 4(3)

DOI: 10.4018/jdwm.2008070103

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Abstract

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

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Vertical Fragmentation in Databases Using Data-Mining Technique

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