Telescope: Zooming to Interesting Skylines

  • Jongwuk Lee
  • Gae-won You
  • Seung-won Hwang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4443)


As data of an unprecedented scale are becoming accessible, skyline queries have been actively studied lately, to retrieve “interesting” data objects that are not dominated by any other objects, i.e., skyline objects. When the dataset is high-dimensional, however, such skyline objects are often too numerous to identify truly interesting objects. This paper studies the “curse of dimensionality” problem in skyline queries. That is, our work complements existing research efforts to address this “curse of dimensionality”, by ranking skyline objects based on user-specific qualitative preference. In particular, Algorithm Telescope abstracts skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference with correctness and optimality guarantees. Our extensive evaluation results validate the effectiveness and efficiency of Algorithm Telescope on both real-life and synthetic data.


User Preference Graph Transformation Skyline Query Lattice Graph Dynamic Search 
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.
    Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: VLDB (2005)Google Scholar
  2. 2.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: A semantic approach based on decisive subspaces. In: VLDB (2005)Google Scholar
  3. 3.
    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
  4. 4.
    Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skyline in high dimensional space. In: SIGMOD (2006)Google Scholar
  5. 5.
    Fagin, R.: Combining fuzzy information from multiple systems. In: PODS 1996, pp. 216–226 (1996)Google Scholar
  6. 6.
    Fagin, R., Lote, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS (2001)Google Scholar
  7. 7.
    Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. In: ICDE (2002)Google Scholar
  8. 8.
    Chang, K.C., Hwang, S.-w.: Minimal probing: Supporting expensive predicates for top-k queries. In: SIGMOD 2002, pp. 346–357 (2002)Google Scholar
  9. 9.
    Hwang, S.-w., Chang, K.C.: Optimizing access cost for top-k queries over web sources. In: ICDE (2005)Google Scholar
  10. 10.
    Yu, H., Hwang, S.-w., Chang, K.C.-C.: RankFP: A framework for supporting rank formulation and processing. In: ICDE (2005)Google Scholar
  11. 11.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE (2001)Google Scholar
  12. 12.
    Kossmann, D.: Shooting stars in the sky: An online algorithm for skyline queries. In: VLDB (2002)Google Scholar
  13. 13.
    Chomicki, J., Godfery, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE (2003)Google Scholar
  14. 14.
    Lee, J., You, G.-w., Hwang, S.-w.: Telescope: Zooming to interesting skylines. POSTECH Technical Report (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jongwuk Lee
    • 1
  • Gae-won You
    • 1
  • Seung-won Hwang
    • 1
  1. 1.POSTECH, PohangKorea

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