Preprocessing Large Data Sets by the Use of Quick Sort Algorithm

  • Marcin Woźniak
  • Zbigniew Marszałek
  • Marcin Gabryel
  • Robert K. Nowicki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 364)


Sorting algorithms help to organize large amounts of data. However, sometimes it is not easy to determine the correct order in large data sets, especially if there are special poses on the input. It often complicates sorting, results in time prolongation or even unable sorting. In such situations, the most common method is to perform sorting process to reshuffled input data or change the algorithm. In this paper, the authors examined quick sort algorithm in two versions for large data sets. The algorithms have been examined in performance tests and the results helped to compare them.


Computer algorithm Data sorting Data mining Analysis of computer algorithms 


  1. 1.
    Aho, I.A., Hopcroft, J., Ullman, J.: The design and analysis of computer algorithms. Addison-Wesley Publishing Company, USA (1975)Google Scholar
  2. 2.
    Bing-Chao, H., Knuth, D.E.: A one-way, stack less Quick sort algorithm. BIT 26, 127–130 (1986)CrossRefMathSciNetzbMATHGoogle Scholar
  3. 3.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. The MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  4. 4.
    Francis, R.S., Pannan, L.J.H.: A parallel partition for enhanced parallel quick sort. Parallel Comput. 18(5), 543–550 (1992)CrossRefzbMATHGoogle Scholar
  5. 5.
    Gedigaa, G., Duntschb, I.: Approximation quality for sorting rules. Comput. Stat. Data Anal. 40, 499–526 (2002)CrossRefGoogle Scholar
  6. 6.
    Knuth, D.E.: The Art of Computer Programming. Addison-Wesley, USA (1998)Google Scholar
  7. 7.
    LaMarca, A., Ladner, R.E.: The influence of caches on the performance of sorting. In: Proceedings of ACM-SIAM Symposium on Discrete Algorithms, pp. 370–379 (1997)Google Scholar
  8. 8.
    Larson, P., Graefe, G.: Memory management during run generation in external sorting. In: Proceedings of SIGMOD, pp. 472–483 (1998)Google Scholar
  9. 9.
    Larson, P.: External sorting: run formation revisited. IEEE Trans. Knowl. Data Eng. 15(4), 961–972 (2003)CrossRefGoogle Scholar
  10. 10.
    MacIlroy, M.: A killer adversary for quick sort. Softw. Pract. Exp. 29, 1–4 (1999)CrossRefGoogle Scholar
  11. 11.
    Marszałek, Z., Połap, D., Woźniak, M.: On preprocessing large data sets by the use of triple merge sort algorithm. In: Proceedings of International Conference on Advances in Information Processing and Communication Technology, The IRED Digital Seek Library (2014) (accepted—in press)Google Scholar
  12. 12.
    Marszałek, Z., Woźniak, M.: On possible organizing Nosql database systems. Int. J. Inf. Sci. Intell. Syst. 2(2), 51–59 (2013)Google Scholar
  13. 13.
    Martynez, C., Roura, C.: Optimal sampling strategies in quick sort and quick select. SIAM J. Comput. 31(3), 683–705 (2002)CrossRefGoogle Scholar
  14. 14.
    Pan, Y., Hamdi, M.: Quick sort on a linear array with a reconfigurable pipelined bus system. Working Paper 94–03, The Center for Business and Economics Research, University of Dayton (1994)Google Scholar
  15. 15.
    Raghaven, P.: Lecture Notes on Randomized Algorithms. Technical Report, IBM Research Division, New York (1990)Google Scholar
  16. 16.
    Rashid, L., Hassanein, W.M., Hammad, M.A.: Analyzing and Enhancing the Parallel Sort Operation on Multithreaded Architectures. J. Supercomput. Springer, Berlin Heidelberg (2009)Google Scholar
  17. 17.
    Rauh, A., Arce, G.R.: A fast weighted median algorithm based on quick select. In: Proceedings of IEEE International Conference on Image Processing, pp. 105–108. Hong Kong (2010)Google Scholar
  18. 18.
    Sedgewick, R.: Implementing quick sort programs. Commun. ACM 21(10), 847–857 (1978)CrossRefzbMATHGoogle Scholar
  19. 19.
    Trimananda, R., Haryanto, C.Y.: A parallel implementation of hybridized merge-quick sort algorithm on MPICH. In: Proceedings of International Conference on Distributed Framework for Multimedia Applications (2010)Google Scholar
  20. 20.
    Tsigas, P., Zhang, Y.: A simple, fast parallel implementation of quick sort and its performance evaluation on SUN enterprise 10000. In: Proceedings of Euromicro Workshop on Parallel, Distributed and Network-Based Processing, pp. 372–381 (2003)Google Scholar
  21. 21.
    Tsigas, P., Zhang, Y.: Parallel quick sort seems to outperform sample sort on cache coherent shared memory multiprocessors: An evaluation on sun enterprise 10000. Technical report, Chalmers University of Technology (2002)Google Scholar
  22. 22.
    Weiss, M.A.: Data Structure & Algorithm Analysis in C++. Addison Wesley (1999)Google Scholar
  23. 23.
    Woźniak, M., Marszałek, Z., Gabryel, M., Nowicki R.K.: Modified merge sort algorithm for large scale data sets. Lecture Notes in Artificial Intelligence, Part II, vol. 7895, pp. 612–622. Springer, Berlin (2013)Google Scholar
  24. 24.
    Woźniak, M., Marszałek, Z., Gabryel, M., Nowicki R.K.: On quick sort algorithm performance for large data sets. In: Skulimowski, A.M.J. (ed.) Looking into the Future of Creativity and Decision Support Systems, pp. 647–656. Progress & Business Publishers, (KICSS’2013), Poland (2013)Google Scholar
  25. 25.
    Woźniak, M., Marszałek, Z., Gabryel, M., Nowicki R.K.: Triple heap sort algorithm for large data sets. In: Skulimowski, A.M.J. (ed.) Looking into the Future of Creativity and Decision Support Systems, pp. 657–665. Progress & Business Publishers, (KICSS’2013), Poland (2013)Google Scholar
  26. 26.
    Woźniak, M., Marszałek, Z.: Selected Algorithms for Sorting Large Data Sets. Silesian University of Technology Press, Poland (2013)Google Scholar
  27. 27.
    Woźniak, M., Marszałek, Z.: Extended Algorithms for Sorting Large Data Sets. Silesian University of Technology Press, Poland (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marcin Woźniak
    • 1
  • Zbigniew Marszałek
    • 1
  • Marcin Gabryel
    • 2
  • Robert K. Nowicki
    • 2
  1. 1.Institute of Mathematics, Silesian University of TechnologyGliwicePoland
  2. 2.Institute of Computational Intelligence, Czestochowa University of TechnologyCzestochowaPoland

Personalised recommendations