Advertisement

Discovering Representative Skyline Points over Distributed Data

  • Akrivi Vlachou
  • Christos Doulkeridis
  • Maria Halkidi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7338)

Abstract

Skyline queries help users make intelligent decisions over complex data. The main shortcoming of skyline queries is that the cardinality of the result set is not known a-priori. To overcome this limitation, the representative skyline query has been proposed, which retrieves a fixed set of k skyline points that best describe all skyline points. Even though the representative skyline has been studied before in centralized environments, this is the first paper that addresses efficient computation of the representative skyline in distributed systems. The distributed nature of the environment makes the task of discovering truly representative skyline points even more challenging. In this paper, we propose a novel framework for discovering the representative skyline over distributed data sources. Our experimental study demonstrates the efficiency and effectiveness of our framework.

Keywords

Representative Point Skyline Query Skyline Point Communication Phase Local Skyline 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proc. of ICDE (2001)Google Scholar
  2. 2.
    Chan, C.Y., Jagadish, H.V., Tan, K.L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: Proc. of SIGMOD (2006)Google Scholar
  3. 3.
    Chaudhuri, S., Gravano, L.: Evaluating top-k selection queries. In: Proc. of VLDB (1999)Google Scholar
  4. 4.
    Cui, B., Lu, H., Xu, Q., Chen, L., Dai, Y., Zhou, Y.: Parallel distributed processing of constrained skyline queries by filtering. In: Proc. of ICDE (2008)Google Scholar
  5. 5.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Proc. of PODS (2001)Google Scholar
  6. 6.
    Hose, K., Vlachou, A.: A survey of skyline processing in highly distributed environments. VLDBJ (2011) (to appear)Google Scholar
  7. 7.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representative skyline operator. In: Proc. of ICDE (2007)Google Scholar
  8. 8.
    Lu, H., Jensen, C.S., Zhang, Z.: Flexible and efficient resolution of skyline query size constraints. IEEE TKDE 23(7), 991–1005 (2011)Google Scholar
  9. 9.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM TODS 30(1), 41–82 (2005)CrossRefGoogle Scholar
  10. 10.
    Sarma, A.D., Lall, A., Nanongkai, D., Lipton, R.J., Xu, J.J.: Representative skylines using threshold-based preference distributions. In: Proc. of ICDE (2011)Google Scholar
  11. 11.
    Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: Proc. of ICDE (2009)Google Scholar
  12. 12.
    Vlachou, A., Doulkeridis, C., Kotidis, Y., Vazirgiannis, M.: SKYPEER: Efficient subspace skyline computation over distributed data. In: Proc. of ICDE (2007)Google Scholar
  13. 13.
    Vlachou, A., Doulkeridis, C., Kotidis, Y., Vazirgiannis, M.: Efficient routing of subspace skyline queries over highly distributed data. IEEE TKDE 22(12), 1694–1708 (2010)Google Scholar
  14. 14.
    Zhu, L., Tao, Y., Zhou, S.: Distributed skyline retrieval with low bandwidth consumption. IEEE TKDE 21(3), 384–400 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Akrivi Vlachou
    • 1
  • Christos Doulkeridis
    • 1
    • 2
  • Maria Halkidi
    • 2
  1. 1.Norwegian University of Science and Technology (NTNU)Norway
  2. 2.University of PiraeusGreece

Personalised recommendations