Application of data mining techniques to mixed fragmentation design in relational database

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Application of data mining techniques to mixed fragmentation design in relational database


Author: Law, Dik-man
Title: Application of data mining techniques to mixed fragmentation design in relational database
Degree: M.Sc.
Year: 2000
Subject: Relational databases
Data mining
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: ix, 71 leaves : ill. ; 30 cm
Language: English
InnoPac Record:
Abstract: The operating efficiency and performance of a relational database is greatly dependent on the effectiveness of the physical design. Every database designer has the ultimate goal to provide optimal physical structures in order to minimize the time spent in I/O operations. One of the techniques used to enhance database performance is attribute partitioning. Attribute partitioning is the process of subdividing the attributes of a relation and then grouping them into fragments so as to minimize the number of disk access by all transactions. There has been a large amount of work on the design of algorithms for attribute partitioning. However, most of them ignore another useful way that can enhance database performance - tuple clustering. Tuple clustering is the process of rearranging the order of tuples so that frequently queried tuples are grouped into as few blocks as possible. Previous experimental results show that the optimal attribute partitioning scheme cannot be obtained without considering the tuple clustering. Also, the optimal tuple clustering solution obtained when assuming no attribute partitioning will no longer be the optimal solution when attribute partitioning is performed. In this paper, we address the need of considering the n-ary attribute partitioning and tuple clustering at the same time in centralised relational database. A new algorithm is proposed for this mixed fragmentation design using genetic algorithm. The algorithm starts from applying genetic algorithm on attribute partitioning for a given tuple clustering scheme. Next, the genetic algorithm is applied on tuple clustering for a given attribute partitioning scheme. This cycle is repeated until there is no further improvement in the solution obtained. Java programs have been developed to implement the genetic algorithm for mixed fragmentation and the results are promising. It provides an improvement over previous works which considered vertical partitioning and tuple clustering separately. The experimental results demonstrated the convergence and performance of our algorithm. Comparisons with exhaustive enumeration and random search are also presented.

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