Advertisement

Preferences Chain Guided Search and Ranking Refinement

  • Yann Loyer
  • Isma Sadoun
  • Karine Zeitouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8055)

Abstract

Preference queries aim at increasing personalized pertinence of a selection. The most famous ones are the skyline queries based on the concept of dominance introduced by Pareto. Many other dominances have been proposed. In particular, many weaker forms of dominance aim at reducing the size of the answer of the skyline query. In most cases, applying just one dominance is not satisfying as it is hard to conciliate high pertinence, i.e. a strong dominance, and reasonable size of the selection. We propose to allow the user to decide what dominances are reliable, and what priorities between those dominances should be respected. This can be done by defining a sequence, eventually transfinite, of dominances. According to that sequence, we propose operators that compute progressively the ranking of a dataset by successive application of the dominances without introducing inconsistencies. The principle of progressive refinement provides a great flexibility to the user that can not only dynamically decide to stop the process whenever the results satisfies his/her wishes, but can also navigates in the different levels of ranking and be aware of the level of reliability of each successive refinement. We also define maximal selection and top-k methods, and discuss some experimentations of those operators.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the IEEE 17th International Conference on Data Engineering (ICDE 2001), pp. 421–430 (2001)Google Scholar
  3. 3.
    Chan, C.Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2006), pp. 503–514 (2006)Google Scholar
  4. 4.
    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
  5. 5.
    Chomicki, J.: Preference formulas in relational queries, vol. 28, pp. 427–466. ACM, New York (2003)Google Scholar
  6. 6.
    Figueira, J., Mousseau, V., Roy, B.: Electre methods. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 133–162. Springer (2005)Google Scholar
  7. 7.
  8. 8.
    Lee, J., You, G.-W., Hwang, S.-W.: Telescope: Zooming to interesting skylines. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 539–550. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: The k most representative skyline operator. In: Proceedings of the 23rd International Conference on Data Engineering (ICDE 2007), pp. 86–95 (2007)Google Scholar
  10. 10.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, SIGMOD 2003, pp. 467–478. ACM, New York (2003)CrossRefGoogle Scholar
  11. 11.
    Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems, vol. 36, pp. 19:1–19:45. ACM, New York (2011)Google Scholar
  12. 12.
    Vlachou, A., Vazirgiannis, M.: Ranking the sky: Discovering the importance of skyline points through subspace dominance relationships, vol. 69, pp. 943–964. Elsevier Science Publishers B. V., Amsterdam (2010)Google Scholar
  13. 13.
    Xia, T., Zhang, D., Tao, Y.: On skylining with flexible dominance relation. In: Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE 2008), pp. 1397–1399 (2008)Google Scholar
  14. 14.
    Yang, J., Fung, G.P., Lu, W., Zhou, X., Chen, H., Du, X.: Finding superior skyline points for multidimensional recommendation applications, vol. 15, pp. 33–60. Kluwer Academic Publishers, HinghamGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yann Loyer
    • 1
  • Isma Sadoun
    • 1
  • Karine Zeitouni
    • 1
  1. 1.PRiSM, CNRS UMR 8144Université de Versailles Saint QuentinFrance

Personalised recommendations