Transitivity-Preserving Skylines for Partially Ordered Domains

  • Henning Köhler
  • Kai Zheng
  • Jing Yang
  • Xiaofang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


The skyline of a set P of multi-dimensional points (tuples) consists of those points in P for which no clearly better point in P exists, using component-wise comparison on domains of interest. The guiding idea is to prune large data sets to a more manageable size, while ensuring that points of interest are preserved. However, when domains are only partially ordered, it easily happens that the skyline is nearly as large as the original set (or at least of the same order of magnitude), since most of the time points are incomparable in at least some dimension.

To obtain a smaller, more useful skyline set which better reflects actual user preferences, we propose a richer notion of dominance, based on two assumptions: that preference specifications are often incomplete, and that actual preferences are transitive.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balke, W.-T., Güntzer, U., Lofi, C.: Eliciting matters - controlling skyline sizes by incremental integration of user preferences. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 551–562. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Balke, W.-T., Güntzer, U., Siberski, W.: Exploiting indifference for customization of partial order skylines. In: IDEAS, pp. 80–88 (2006)Google Scholar
  3. 3.
    Balke, W.-T., Siberski, W., Güntzer, U.: Getting prime cuts from skylines over partially ordered domains. In: BTW, pp. 64–81 (2007)Google Scholar
  4. 4.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)Google Scholar
  5. 5.
    Carey, M.J., Kossmann, D.: On saying “enough already!” in sql. In: SIGMOD, pp. 219–230 (1997)Google Scholar
  6. 6.
    Chan, C.Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: SIGMOD, pp. 503–514 (2006)Google Scholar
  7. 7.
    Chan, C.Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On high dimensional skylines. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 478–495. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Georgiadis, P., Kapantaidakis, I., Christophides, V., Nguer, E.M., Spyratos, N.: Efficient rewriting algorithms for preference queries. In: ICDE, pp. 1101–1110 (2008)Google Scholar
  9. 9.
    Koltun, V., Papadimitriou, C.H.: Approximately dominating representatives. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 204–214. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: The k most representative skyline operator. In: ICDE, pp. 86–95 (2007)Google Scholar
  11. 11.
    Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: ICDE, pp. 892–903 (2009)Google Scholar
  12. 12.
    Xia, T., Zhang, D., Tao, Y.: On skylining with flexible dominance relation. In: ICDE, pp. 1397–1399 (2008)Google Scholar
  13. 13.
    Yiu, M.L., Mamoulis, N.: Efficient processing of top-k dominating queries on multi-dimensional data. In: VLDB, pp. 483–494 (2007)Google Scholar
  14. 14.
    Zhang, Z., Guo, X., Lu, H., Tung, A.K.H., Wang, N.: Discovering strong skyline points in high dimensional spaces. In: CIKM, pp. 247–248 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Henning Köhler
    • 1
  • Kai Zheng
    • 1
  • Jing Yang
    • 1
  • Xiaofang Zhou
    • 1
  1. 1.The University of QueenslandBrisbaneAustralia

Personalised recommendations