Skip to main content

Advertisement

SpringerLink
Log in

Improving database performance with a mixed fragmentation design

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The authors are thankful to the reviewer that provided this feedback.

References

  • Ailamaki, A., Dewitt, D.J., Hill, M. D., & Skounakis, M. (2001). Weaving relations for cache performance. In Proceedings of the 27th VLDB conference.

  • Baiao, F., Mattoso, M., & Zaverucha, G. (2004). A distribution design methodology for object DBMS. Journal of Distributed and Parallel Databases, 16(6), 45–90.

    Article  Google Scholar 

  • Ceri, S., Navathe, S., & Wiederhold, G. (1983). Distribution design of logical database schemas. IEEE Transactions on Software Engineering, 9(4), 487–504.

    Article  Google Scholar 

  • Chambers, L. (1995). Practical handbook of genetic algorithms (Vol. 1). CRC Press.

  • Cheng, C. H., Lee, W. K., & Wong, K. F. (2002). A genetic algorithm-based clustering approach for database partitioning. IEEE Transactions on Systems, Man, and Cybernetics, 32(3), 215–230.

    Article  Google Scholar 

  • Chu, W. W., & Ieong, I. T. (1993). A transaction-based approach to vertical partitioning for relational database sytems. IEEE Transactions on Systems, Man, and Cybernetics, 19(8), 804–812.

    Google Scholar 

  • Cornell, D. W., & Yu, P. S. (1990). An effective approach to vertical partitioning for physical design of relational databases. IEEE Transactions on Software Engineering, 16(22), 248–258.

    Article  Google Scholar 

  • Du, J., Alhajj, R., & Barker, K. (2006). Genetic algorithms based approach to database vertical partitioning. Journal of Intelligent Information Systems, 26(2), 167–183.

    Article  Google Scholar 

  • Eisner, M. J., & Severance, D. G. (1976). Mathematical techniques for efficient record segmentation in large shared database. Journal of ACM, 23(4), 619–635.

    Article  MathSciNet  MATH  Google Scholar 

  • Ezeife, C. I. (2001). Selecting and materializing horizontally partitioned warehouse views. Data and Knowledge Engineering, 36, 185–210.

    Article  MATH  Google Scholar 

  • Franti, P., Kivijarvi, J., Kaukoranta, T., & Nevalainen, O. (1997). Genetic algorithm for large-scale clustering problems. Computer Journal, 40(9), 547–554.

    Article  Google Scholar 

  • Fung, C. W., Karlapalem, K., & Li, Q. (2002). An evaluation of vertical class partitioning for query processing in object-oriented databases. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1095–1118.

    Article  Google Scholar 

  • Furtado, C., Lima, A. A. B., Pacitti, E., Valduriez, P., & Mattoso, M. (2005). Physical and virtual partitioning in OLAP database cluster. In 17th international symposium on computer architecture and high performance computing (pp. 143–150).

  • Goldberg, D. E. (1989). Genetic algorithm in search, optimization, and machine learning. Addison-Wesley.

  • Gorla, N. (2001). An object-oriented database design for improved performance. Data and Knowledge Engineering, 37(2), 117–138.

    Google Scholar 

  • Gorla, N. (2007). A methodology for vertically partitioning in a multi-relation database environment. Journal of Computer Science & Technology, 7(3), 217–227.

    Google Scholar 

  • Gorla, N., & Betty, P. W. Y. (2008). Vertical fragmentation in databases using data-mining technique. International Journal of Data Warehousing & Mining, 4(3), 35–53.

    Article  Google Scholar 

  • Gorla, N., & Boe, W. (1990). Database performance of fragmented databases in mainframe, Mini, and micro computer systems. Data and Knowledge Engineering, 5(1), 1–19.

    Article  Google Scholar 

  • Gorla, N., & Quinn, W. (1991). Combined optimal tuple ordering and attribute partitioning in storage schema design. Information and Software Technology, 33(5), 335–339.

    Article  Google Scholar 

  • Gorla, N., & Song, S. K. (2010). Sub query allocations in distributed databases using genetic algorithms. Journal of Computer Science & Technology 10(1), 31–37.

    Google Scholar 

  • Hoffer, J. A., & Serverance, D. G. (1975). The use of cluster analysis in physical design. In Proc. first international conference on very large data bases.

  • Knuth, D. (1973). Sorting and searching. The art of computer programming (Vol. 3). Addison-Welsey.

  • Li, B., & Jiang, W. (2000). A novel stochastic optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 30(1), 193–198.

    Article  Google Scholar 

  • Mahboubi, H., & Darmont, J. (2008). Data mining-based fragmentation of XML data warehouses. In DOLAP ’08.

  • March, S. T., & Rho, S. (1995). Allocating data and operations to nodes in distributed database design. IEEE Transactions on Knowledge and Data Engineering, 7(2), 305–317.

    Article  Google Scholar 

  • Navathe, S., Ceri, S., Wiederhold, G., & Dou, J. (1984). Vertical partitioning algorithms for database design. ACM Transactions on Database Systems, 9(4), 680–710.

    Article  Google Scholar 

  • Ng, V., Law, D. M., Gorla N., & Chan, C. K. (2003). Applying genetic algorithms in database partitioning. In 2003 ACM symposium on applied computing (SAC).

  • Niamir, B. (1978). Attribute partitioning in a self-adaptive relational database system. PhD Dissertation, MIT Lab. for Computer Science, Jan 1978.

  • Ozsu, M., & Valduriez, P. (1996). Principles of database systems. Prentice Hall.

  • Ramamurthy, R., Dewitt, D. J., & Su, Q. (2002). A case for fractured mirrors. In Proceedings of the 28th VLDB conference.

  • Rivest, R. (1976). On self-organizing sequential search heuristics. Communication of the ACM, 19(2), 63–67.

    Article  MathSciNet  MATH  Google Scholar 

  • Serban, G., & Campan, A. (2008). Hierarchical adaptive clustering. Informatica, 19(1), 101–112.

    MathSciNet  MATH  Google Scholar 

  • Song, S. K., & Gorla, N. (2000). A genetic algorithm for vertical fragmentation and access path selection. The Computer Journal, 45(1), 81–93.

    Article  Google Scholar 

  • Tam, K. Y. (1992). Genetic algorithms, function optimization, and facility layout design. European Journal of Operations Research, 63(2), 322–346.

    Article  MATH  Google Scholar 

  • Tamhanker, A. J., & Ram, S. (1998). Database fragmentation and allocation: An integrated methodology and case study. IEEE Transactions on Man, Systems, and Cybernetics, 28(3), 288–305.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. American University of Sharjah, PO Box 26666, Sharjah, UAE

    Narasimhaiah Gorla

  2. Hong Kong Polytechnic University, Hong Kong, China

    Vincent Ng & Dik Man Law

Authors
  1. Narasimhaiah Gorla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dik Man Law
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Narasimhaiah Gorla.

Additional information

An earlier version of this paper has been presented at the 2003 ACM Symposium on Applied Computing (SAC)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gorla, N., Ng, V. & Law, D.M. Improving database performance with a mixed fragmentation design. J Intell Inf Syst 39, 559–576 (2012). https://doi.org/10.1007/s10844-012-0203-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-012-0203-x

Keywords

Search

Navigation