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Data Mining-based Fragmentation for Query Optimization

Sridharan, Srilakshmi
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1397467744

Abstract Details

2014, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
A main purpose of a database is to provide requested data efficiently. Query performance can be improved in many ways. One of the efficient ways to handle multiple queries posted simultaneously to the database is to distribute the database across several sites and instead of querying the entire database, only the site that contains the data related to the query is accessed. Distribution of a database involves fragmentation of the data and allocating the fragmented data across various sites. Several research works address the issue of fragmentation of databases based on workload, since the aim of fragmentation is to optimize query response time [MD08]. In particular, clustering the data according to query predicates or attributes is shown to perform well for fragmentation. Mahboubi and Darmont propose the use of a k-means based fragmentation approach [MD08]. The authors do not consider the similarity of query predicates in the workload before performing the k-means clustering in their approach. We cluster similar selection predicates involved in the workload as a pre-processing step for the fragmentation; we expect to further improve the query performance. We investigate clustering techniques and study the resulting performance for a selected case study. We conclude that in general for our workloads and for our experimental parameters, the final clusters obtained using our predicate preprocessing system are tighter and more meaningful. As the number of similar values in the workload decreases, the relative savings of the predicate preprocessing system is reduced. If there are no similar values in the workload, the original fragmentation system is more efficient.
Karen Davis, Ph.D. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Carla Purdy, Ph.D. (Committee Member)
83 p.
Computer Science
Data Mining-based Fragmentation; Query Optimation; Fragmentation; Distributed Databases; Clustering; Cluster Similar Predicates

Recommended Citations

Citations

  • Sridharan, S. (2014). Data Mining-based Fragmentation for Query Optimization [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1397467744

    APA Style (7th edition)

  • Sridharan, Srilakshmi. Data Mining-based Fragmentation for Query Optimization. 2014. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1397467744.

    MLA Style (8th edition)

  • Sridharan, Srilakshmi. "Data Mining-based Fragmentation for Query Optimization." Master's thesis, University of Cincinnati, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1397467744

    Chicago Manual of Style (17th edition)

ucin1397467744
494
© 2014, some rights reserved.Creative Commons License Data Mining-based Fragmentation for Query Optimization by Srilakshmi Sridharan is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.