Computing Skyline Incrementally in Response to Online Preference Modification

  • Tassadit Bouadi
  • Marie-Odile Cordier
  • René Quiniou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8220)


Skyline queries retrieve the most interesting objects from a database with respect to multi-dimensional preferences. Identifying and extracting the relevant data corresponding to multiple criteria provided by users remains a difficult task, especially when the dataset is large. EC 2 Sky, our proposal, focuses on how to answer efficiently skyline queries in the presence of dynamic user preferences and despite large volumes of data. In 2008-2009, Wong et al. showed that the skyline associated with any preference on a particular dimension can be computed, without domination tests, from the skyline points associated with first order preferences on that same dimension. Consequently, they propose to materialize skyline points associated with the most preferred values in a specific data structure called IPO-tree (Implicit Preference Order Tree). However, the size of the IPO-tree is exponential with respect to the number of dimensions. While reusing the merging property proposed by Wong et al. to deal with the refinements of preferences on a single dimension, we propose an incremental method for calculating the skyline points related to several dimensions associated with dynamic preferences. For this purpose, a materialization of linear size which allows a great flexibility for dimension preference updates is defined. This contribution improves notably the execution time and storage size of queries. Experiments on synthetic data highlight the relevance of EC 2 Sky compared to IPO-Tree.


Dynamic Dimension Order Preference Skyline Query Storage Size Incremental Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balke, W.T., Guntzer, U., Siberski, W.: Exploiting indifference for customization of partial order skylines. In: Proceedings of the 10th International Database Engineering and Applications Symposium, pp. 80–88. IEEE Computer Society (2006)Google Scholar
  2. 2.
    Bentley, J.L., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. J. ACM 25(4), 536–543 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bitran, G.R., Magnanti, T.L.: The structure of admissible points with respect to cone dominance. Optimization Theory and Applications 29(4), 573–614 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proc. of the 17th International Conference on Data Engineering, pp. 421–430. IEEE Computer Society (2001)Google Scholar
  5. 5.
    Bouadi, T., Cordier, M.-O., Quiniou, R.: Incremental computation of skyline queries with dynamic preferences. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part I. LNCS, vol. 7446, pp. 219–233. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Brando, C., Goncalves, M., González, V.: Evaluating top-k skyline queries over relational databases. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 254–263. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Chen, L., Lian, X.: Efficient processing of metric skyline queries. IEEE Trans. on Knowl. and Data Eng. 21(3), 351–365 (2009)CrossRefGoogle Scholar
  8. 8.
    Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting: Theory and optimizations. In: Proc. of Intelligent Information Systems, pp. 595–604. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Godfrey, P., Shipley, R., Gryz, J.: Algorithms and analyses for maximal vector computation. The VLDB Journal 16(1), 5–28 (2007)CrossRefGoogle Scholar
  10. 10.
    Huang, Z., Guo, J., Sun, S.L., Wang, W.: Efficient optimization of multiple subspace skyline queries. J. Comput. Sci. Technol. 23(1), 103–111 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jin, W., Tung, A.K.H., Ester, M., Han, J.: On efficient processing of subspace skyline queries on high dimensional data. In: Proc. of the 19th International Conference on Scientific and Statistical Database Management. IEEE Computer Society (2007)Google Scholar
  12. 12.
    Mindolin, D., Chomicki, J.: Preference elicitation in prioritized skyline queries. The VLDB Journal 20(2), 157–182 (2011)CrossRefGoogle Scholar
  13. 13.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)CrossRefGoogle Scholar
  14. 14.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: Proc of the 31st International Conference on Very Large Data Bases, pp. 253–264, VLDB Endowment (2005)Google Scholar
  15. 15.
    Raïssi, C., Pei, J., Kister, T.: Computing closed skycubes. Proc. VLDB Endow. 3(1), 838–847 (2010)Google Scholar
  16. 16.
    Sawaragi, Y., Nakayama, H., Tanino, T.: Theory of Multiobjective Optimization. Academic Press, Orlando (1985)zbMATHGoogle Scholar
  17. 17.
    Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. In: Proceedings of the 27th International Conference on Very Large Data Bases, pp. 301–310. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  18. 18.
    Tao, Y., Xiao, X., Pei, J.: Efficient skyline and top-k retrieval in subspaces. IEEE Trans. on Knowl. and Data Eng. 19(8), 1072–1088 (2008)CrossRefGoogle Scholar
  19. 19.
    Trenkler, G.: In: Johnson, N.l., Kotz, S., kemp, A.W. (eds.) Univariate Discrete Distributions, 2nd edn. John wiley (1994) ISBN 0-471-54897-9; Computational Statistics & Data Analysis, 17(2), 240–241 (1994) Google Scholar
  20. 20.
    Wong, R.C.W., Fu, A.W.C., Pei, J., Ho, Y.S., Wong, T., Liu, Y.: Efficient skyline querying with variable user preferences on nominal attributes. Proc. VLDB Endow. 1(1), 1032–1043 (2008)Google Scholar
  21. 21.
    Wong, R.C.W., Pei, J., Fu, A.W.C., Wang, K.: An erratum on “online skyline analysis with dynamic preferences on nominal attributes”. IEEE Trans. on Knowl. and Data Eng. (to be published)Google Scholar
  22. 22.
    Wong, R.C.W., Pei, J., Fu, A.W.C., Wang, K.: Online skyline analysis with dynamic preferences on nominal attributes. IEEE Trans. on Knowl. and Data Eng. 21(1), 35–49 (2009)CrossRefGoogle Scholar
  23. 23.
    Xia, T., Zhang, D., Tao, Y.: On skylining with flexible dominance relation. In: Proc. of the 2008 IEEE 24th International Conference on Data Engineering, pp. 1397–1399. IEEE Computer Society (2008)Google Scholar
  24. 24.
    Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: Proc. of the 31st International Conference on Very Large Data Bases, pp. 241–252, VLDB Endowment (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tassadit Bouadi
    • 1
  • Marie-Odile Cordier
    • 1
  • René Quiniou
    • 2
  1. 1.IRISA - University of Rennes 1France
  2. 2.IRISA - INRIA RennesRennesFrance

Personalised recommendations