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Recent Advancements in Parallel Algorithms for String Matching on Computing Models – A Survey and Experimental Results

  • Chinta Someswararao
  • K. Butchi Raju
  • S. V. Appaji
  • S. Viswanadha Raju
  • K. K. V. V. V. S. Reddy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7135)

Abstract

The increase in huge amount of data is seen clearly in present days because of requirement for storing more information. To extract certain data from this large database is a very difficult task, including text processing, information retrieval, text mining, pattern recognition and DNA sequencing. So we need concurrent events and high performance computing models for extracting the data. This will create a challenge to the researchers. One of the solutions is parallel algorithms for string matching on computing models. This work reviews typical algorithms and profiles their performance under various situations to study the influence of the number, the length, and the character distribution. This paper provides various available techniques that are suggested by several authors with its merits and demerits. In this we say that this survey will help the researchers to develop a better.

Keywords

Text processing IRS computing models string matching parallel algorithms 

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References

  1. 1.
    Grabowski, K.S.: Average-Optimal String Matching. Journal of Discrete Algorithms, 579–594 (2009)Google Scholar
  2. 2.
    Russo, L.L., Navarro, G., Oliveira, A., Morales, P.: Approximate String Matching with Compressed Indexes Algorithm, pp. 1105–1136 (2009)Google Scholar
  3. 3.
    Ilie, L., Navarro, G., Tinta, L.: The Longest Common Extension Problem, Revisited and Applications to Approximate String Searching. Journal of Discrete Algorithms, 418–428 (2010)Google Scholar
  4. 4.
    Fredriksson, K., Grabowski, S.: Average-Optimal String Matching. Journal of Discrete Algorithms, 579–594 (2009)Google Scholar
  5. 5.
    Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation. Numerical Methods. Prentice-HallGoogle Scholar
  6. 6.
    Mohr, B.: Introduction to Parallel Computing. John von Neumann Institute for Computing (2006)Google Scholar
  7. 7.
    Kang, H.K.S.: A Pattern Group Partitioning for Parallel String Matching using a Pattern Grouping Metric. IEEE Communications Letters, 878–880 (2010)Google Scholar
  8. 8.
    Kim, H.J., Kim, H.S., Kang, S.: A Memory-Efficient Bit-Split Parallel String Matching Using Pattern Dividing for Intrusion Detection Systems. IEEE Transactions on Parallel and Distributed Systems (2011)Google Scholar
  9. 9.
    Viswanadha, R.S., Vinayababu, A.: Optimal Parallel algorithm for String Matching on Mesh Network Structure (2006)Google Scholar
  10. 10.
    Viswanadha, R.S., Babu, V.A., Mrudula, M.: Backend Engine for Parallel String Matching Using Boolean Matrix. In: IEEE on PAR ELEC, pp. 281–283 (2006)Google Scholar
  11. 11.
    Peltola, H., Tarhio, J.: Alternative Algorithms for Bit-Parallel String Matching, Finland (2001)Google Scholar
  12. 12.
    Viswanadha, R.S., Mantena, A.S.R., Babu, V., Raju, G.V.S.: Efficient Parallel Pattern Matching using Partition Method (2006)Google Scholar
  13. 13.
    Viswanadha, R.S., Babu, V.A., Raju, G.V.S., Madhavi, K.R.: W-Period Technique for Parallel String Matching (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chinta Someswararao
    • 1
  • K. Butchi Raju
    • 2
  • S. V. Appaji
    • 2
  • S. Viswanadha Raju
    • 3
  • K. K. V. V. V. S. Reddy
    • 4
  1. 1.Department of CSESRKR Engg CollegeBhimavaramIndia
  2. 2.Department of CSEGRIETHyderabadIndia
  3. 3.School of ITJNTUHHyderabadIndia
  4. 4.Rayalasheema UniversityIndia

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