Skip to main content
SpringerLink
Log in

Shared-Table for Textual Data Clustering in Distributed Relational Databases

  • Conference paper
  • First Online:
Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 285))

  • 3032 Accesses

Abstract

High-performance query processing is a significant requirement of database administrators that can be achieved by grouping data into continuous hard disk pages. Such performance can be achieved by using database partitioning techniques. Database partitioning techniques aid in splitting of the physical structure of database tables into small partitions. A distributed database management system is advantageous for many businesses because such a system aids in the achievement of high-performance processing. However, massive amount of data distributed over network nodes affect query processing when retrieving data from different nodes. This study proposes a novel technique based on a shared-table in a relational database under a distributed environment to achieve high-performance query processing by using data mining techniques. A shared-table is used as a guide to show where the data should be saved. Thus, the efficiency of query processing will improve when data is saved at the same location. The proposed method is suitable for news agencies and domains that rely on massive amount of textual data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abuelyaman, E.S., An Optimized Scheme for Vertical Partitioning of a Distributed Database. IJCSNS International Journal of Computer Science and Network Security, 2008. VOL.8 No.1: p. 310-316.

    Google Scholar 

  2. Khan, S.I. and D.A.S.M.L. Hoque, A New Technique for Database Fragmentation in Distributed Systems. International Journal of Computer Applications, 2010. Volume 5– No.9: p. 0975 – 8887.

    Google Scholar 

  3. Chu, W.W. and I.T. Ieong, A Transaction-Based Approach to Vertical Partitioning for Relational Database Systems. Software Engineering, IEEE Transactions on, 1993. VOL. 19, NO. 8.

    Google Scholar 

  4. Li, L. and L. Gruenwald, Autonomous Database Partitioning using Data Mining on Single Computers and Cluster Computers. Proceedings of the 16th International Database Engineering & Applications Sysmposium. ACM, 2012.

    Google Scholar 

  5. Ma, H., K.-D. Schewe, and M. Kirchberg, A Heuristic Approach to Vertical Fragmentation Incorporating Query Information. Databases and Information Systems, 2006. 7th International Baltic Conference on. IEEE: p. 69-76.

    Google Scholar 

  6. Rodriguez, L. and X. Li, A vertical partitioning algorithm for distributed multimedia databases.. In e. a. A Hameurlain, editor, Proceedings of DEXA,. Springer Verlag, 2011. Vol 6861 (544—558).

    Google Scholar 

  7. RodríguezA, L. and X. Li, A dynamic vertical partitioning approach for distributed database system. Systems, Man, and Cybernetics (SMC), IEEE International Conference on. IEEE, 2011.

    Google Scholar 

  8. Song, S. and N. Gorla, A genetic Algorithm for Vertical Fragmentation and Access Path Selection. The Computer Journal, 2000. vol. 45, no. 1: p. 81-93.

    Google Scholar 

  9. Zhang, Y., On horizontal fragmentation of distributed database design. in M. Orlowska & M. Papazoglou, eds, Advances in Database Re- search, 1993. World Scientific Publishing: p. 121-130.

    Google Scholar 

  10. Ceri, S., M. Negri, and G. Pelagatti, Horizontal data partitioning in database design. in Proc. ACM SIGMOD, 1982.

    Google Scholar 

  11. S. Navathe, K.K., Minyoung Ra, Amixed fragmentation methodology for initial distributed database design. Journal of Computer and Software Engineering 1995. 3.4 (1995): p. 395-426.

    Google Scholar 

  12. Gorla, N., V. Ng, and D.M. Law, Improving database performance with a mixed fragmentation design. J Intell Inf Syst (2012) 39, 2012. 39: p. 559–576.

    Google Scholar 

  13. Hoffer, H.A. and D.G. Severance, The Use of Cluster Analysis in Physical Database Design. Proceedings First Internutionul Conference on Vety Large Data Bases, 1975.

    Google Scholar 

  14. Navathe, S., et al., Vertical partitioning algorithms for database design. ACM Transactions on Database Systems (TODS) 9.4, 1984: p. 680-710.

    Google Scholar 

  15. Navathe, S.B. and M. Ra, Vertical Partitioning for Database Design: A Graphical Algorithm. ACM SIGMOD Record 18.2, 1989.

    Google Scholar 

  16. Ra, M., Horizontal partitioning for distributed database design. In Advances in Database Research, World Scientific Publishing, 1993: p. 101–120.

    Google Scholar 

  17. Ng, V., et al., Applying genetic algorithms in database partitioning. SAC ‘03 Proceedings of the ACM symposium on Applied computing, 2003: p. 544-549.

    Google Scholar 

  18. Ozsu, M.T. and P. Valduriez, Principles of Distributed Database Systems. 2nd ed., New Jersey: Prentice-Hall, 1999.

    Google Scholar 

  19. McCormick, W.T., P.J. Schweitzer, and T.W. White, Problem decomposition and data reorganization by a clustering technique. 1972. Operations Research 20.5: p. 993-1009.

    Google Scholar 

  20. Chakravarthy, S., et al., An objective function for vertically partitioning relations in distributed databases and its analysis. Distributed and parallel databases 2.2 1994. 183-207.

    Google Scholar 

  21. Muthuraj, J., et al., A formal approach to the vertical partitioning problem in distributed database design. Parallel and Distributed Information Systems, Proceedings of the Second International Conference on. IEEE, 1993.

    Google Scholar 

  22. Guinepain, S. and L. Gruenwald, Using Cluster Computing to Support Automatic and Dynamic Database Clustering. Cluster Computing, 2008 IEEE International Conference on. IEEE, 2008.

    Google Scholar 

  23. Rodríguez, L., et al., DYMOND: An Active System for Dynamic Vertical Partitioning of Multimedia Databases. Proceedings of the 16th International Database Engineering & Applications Sysmposium. ACM, 2012., 2012.

    Google Scholar 

  24. Cheng, C.-H., W.-K. Lee, and K.-F. Wong, A Genetic Algorithm-Based Clustering Approach for Database Partitioning. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 2002. VOL. 32, NO. 3: p. 215-230.

    Google Scholar 

  25. Surmsuk, P. and S. Thanawastien, The Integrated Strategic Information System Planning Methodology. 11th IEEE International Enterprise Distributed Object Computing Conference, 2007.

    Google Scholar 

  26. Montalvo, S., F. Víctor, and M. Raquel, NESM: a Named Entity based Proximity Measure for Multilingual News Clustering. Procesamiento de Lenguaje Natural, 2012. 48: p. 81-88.

    Google Scholar 

  27. Cao, T.H., T.M. Tang, and C.K. Chau, Data Mining: Foundations and Intelligent Paradigms Springer Berlin Heidelberg, 2012: p. 267-287.

    Google Scholar 

  28. YafoozB, W.M.S., S.Z. Abidin, and N. Omar, Challenges and issues on online news management. Control System, Computing and Engineering (ICCSCE),IEEE International Conference on., 2011.

    Google Scholar 

  29. Krishna, S.M. and S.D. Bhavani, An Efficient Approach for Text Clustering Based on Frequent Itemsets. European Journal of Scientific Research, 2010. ISSN 1450-216X Vol.42 No.3: p. 399-410.

    Google Scholar 

  30. Beil, F., M. Ester, and X. Xu, Frequent Term-Based Text Clustering. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002.

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank Universiti Teknologi MARA (UiTM) for the financial support. This work was supported in part by a grant number 600-RMI-/DANA 5/3/RIF (498/2012).

Author information

Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, UiTM, Shah Alam, Selagor, Malaysia

    Wael M. S. Yafooz, Siti Z. Z. Abidin, Nasiroh Omar & Rosenah A. Halim

Authors
  1. Wael M. S. Yafooz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siti Z. Z. Abidin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nasiroh Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosenah A. Halim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wael M. S. Yafooz .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

  3. School of Information Technology, Deakin University, Burwood, Victoria, Australia

    Jemal Abawajy

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Singapore

About this paper

Cite this paper

Yafooz, W.M.S., Abidin, S.Z.Z., Omar, N., Halim, R.A. (2014). Shared-Table for Textual Data Clustering in Distributed Relational Databases. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-4585-18-7_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-17-0

  • Online ISBN: 978-981-4585-18-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation