Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

  • Joachim Selke
  • Christoph Lofi
  • Wolf-Tilo Balke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


Until recently algorithms continuously gained free performance improvements due to ever increasing processor speeds. Unfortunately, this development has reached its limit. Nowadays, new generations of CPUs focus on increasing the number of processing cores instead of simply increasing the performance of a single core. Thus, sequential algorithms will be excluded from future technological advances. Instead, highly scalable parallel algorithms are needed to fully tap new hardware potentials. In this paper we establish a design space for parallel algorithms in the domain of personalized database retrieval, taking skyline algorithms as a representative example. We will investigate the spectrum of base operations of different retrieval algorithms and various parallelization techniques to develop a set of highly scalable and high-performing skyline algorithms for different retrieval scenarios. Finally, we extensively evaluate these algorithms to showcase their superior characteristics.


Design Space Shared Memory Main Memory Retrieval Algorithm Skyline Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vitter, J.S.: Algorithms and data structures for external memory. Foundations and Trends in Theoretical Computer Science 2(4), 305–474 (2006)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Larus, J.: Spending Moore’s dividend. Communications of the ACM 52(5), 62–69 (2009)CrossRefGoogle Scholar
  3. 3.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline operator. In: Proceedings of the 17th International Conference on Data Engineering (ICDE 2001), pp. 421–430. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  4. 4.
    Chomicki, J.: Preference formulas in relational queries. ACM Transactions on Database Systems 28(4), 427–466 (2003)CrossRefGoogle Scholar
  5. 5.
    Godfrey, P., Shipley, R., Gryz, J.: Algorithms and analyses for maximal vector computation. The VLDB Journal 16(1), 5–28 (2007)CrossRefGoogle Scholar
  6. 6.
    Morse, M., Patel, J.M., Jagadish, H.V.: Efficient skyline computation over low-cardinality domains. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB 2007), pp. 267–278. ACM Press, New York (2007)Google Scholar
  7. 7.
    Preisinger, T., Kießling, W.: The Hexagon algorithm for Pareto preference queries. In: Proceedings of the 3rd Multidisciplinary Workshop on Advances in Preference Handling, M-PREF 2007 (2007)Google Scholar
  8. 8.
    Eng, P.-K., Ooi, B.C., Tan, K.-L.: Indexing for progressive skyline computation. Data 4 Knowledge Engineering 46(2), 169–201 (2003)CrossRefGoogle Scholar
  9. 9.
    Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Dayal, U., Ramamritham, K., Vijayaraman, T.M. (eds.) Proceedings of the 19th International Conference on Data Engineering (ICDE 2003), pp. 717–719. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  10. 10.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Transactions on Database Systems 30(1), 41–82 (2005)CrossRefGoogle Scholar
  11. 11.
    Bartolini, I., Ciaccia, P., Patella, M.: Efficient sort-based skyline evaluation. ACM Transactions on Database Systems 33(4) (2008)Google Scholar
  12. 12.
    Bentley, J.L., Clarkson, K.L., Levine, D.B.: Fast linear expected-time algorithms for computing maxima and convex hulls. Algorithmica 9(2), 168–183 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Daskalakis, C., Karp, R.M., Mossel, E., Riesenfeld, S., Verbin, E.: Sorting and selection in posets. In: Mathieu, C. (ed.) Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2009), pp. 392–401. SIAM, Philadelphia (2009)Google Scholar
  14. 14.
    Torlone, R., Ciaccia, P.: Finding the best when it’s a matter of preference. In: Ciaccia, P., Rabitti, F., Soda, G. (eds.) Proceedings of the 10th Italian Symposium on Advanced Database Systems (SEBD 2002), pp. 347–360 (2002)Google Scholar
  15. 15.
    Boldi, P., Chierichetti, F., Vigna, S.: Pictures from Mongolia: Extracting the top elements from a partially ordered set. Theory of Computing Systems 44(2), 269–288 (2009)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Park, S., Kim, T., Park, J., Kim, J., Im, H.: Parallel skyline computation on multicore architectures. In: Proceedings of the 25th International Conference on Data Engineering (ICDE 2009), pp. 760–771. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  17. 17.
    Bentley, J.L., Kung, H.-T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. Journal of the ACM 25(4), 536–543 (1978)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Sun, M.: A primogenitary linked quad tree data structure and its application to discrete multiple criteria optimization. Annals of Operations Research 147(1), 87–107 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Bayer, R., Schkolnick, M.: Concurrency of operations on B-trees. Acta Informatica 9(1), 1–21 (1977)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Heller, S., Herlihy, M., Luchang co, V., Moir, M., Scherer III, W.N., Shavit, N.: A lazy concurrent list-based set algorithm. Parallel Processing Letters 17(4), 411–424 (2007)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Harris, T.L.: A pragmatic implementation of non-blocking linked-lists. In: Welch, J.L. (ed.) DISC 2001. LNCS, vol. 2180, pp. 300–314. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. 22.
    Michael, M.M.: High performance dynamic lock-free hash tables and list-based sets. In: Proceedings of the 14th Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA 2002), pp. 73–82. ACM Press, New York (2002)CrossRefGoogle Scholar
  23. 23.
    Levandoski, J., Mokbel, M., Khalefa, M.: FlexPref: A Framework for Extensible Preference Evaluation in Database Systems. In: International Conference on Data Engineering (ICDE), Long Beach, CA, USA (2010)Google Scholar
  24. 24.
    Cosgaya-Lozano, A., Rau-Chaplin, A., Zeh, N.: Parallel computation of skyline queries. In: Proceedings of the 21st International Symposium on High Performance Computing Systems and Applications (HPCS 2007). IEEE Computer Society, Los Alamitos (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joachim Selke
    • 1
  • Christoph Lofi
    • 1
  • Wolf-Tilo Balke
    • 1
  1. 1.Institut für InformationssystemeTechnische Universität BraunschweigBraunschweigGermany

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