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An I/O Optimal and Scalable Skyline Query Algorithm

  • Yunjun Gao
  • Gencai Chen
  • Ling Chen
  • Chun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4042)

Abstract

Given a set of d-dimensional points, skyline query returns the points that are not dominated by any other point on all dimensions. Currently, BBS (branch-and-bound skyline) is the most efficient skyline processing method over static data in a centralized setting. Although BBS has some desirable features (e.g., I/O optimal and flexibility), it requires large main-memory consumption. In this paper, we present an improved skyline computation algorithm based on best-first nearest neighbor search, called IBBS, which captures the optimal I/O and less memory space (i.e., IBBS visits and stores only those entries that contribute to the final skyline). Its core enables several effective pruning strategies to discard non-qualifying entries. Extensive experimental evaluations show that IBBS outperforms BBS in both scalability and efficiency for most cases, especially in low dimensions.

Keywords

Near Neighbor Skyline Query Pruning Strategy Skyline Point Skyline Computation 
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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References

  1. 1.
    Balke, W.-T., Gntzer, U., Zheng, J.X.: Efficient Distributed Skylining for Web Information Systems. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 256–273. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: SIGMOD, pp. 322–331 (1990)Google Scholar
  3. 3.
    Borzsony, S., Kossmann, D., Stocker, K.: The Skyline Operator. In: ICDE, pp. 421–430 (2001)Google Scholar
  4. 4.
    Böhm, C., Kriegel, H.-P.: Determining the Convex Hull in Large Multidimensional Databases. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 265–318. Springer, Heidelberg (2001)Google Scholar
  5. 5.
    Chan, C.-Y., Eng, P.-K., Tan., K.-L.: Stratified Computation of Skylines with Partially-Ordered Domains. In: SIGMOD, pp. 203–214 (2005)Google Scholar
  6. 6.
    Chang, Y.-C., Chang, Y.-C., Bergman, L.D., Castelli, V., Li, C.-S., Lo, M.-L., Smith, J.: The Onion Technique: Indexing for Linear Optimization Queries. In: SIGMOD, pp. 391–402 (2000)Google Scholar
  7. 7.
    Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with Presorting. In: ICDE, pp. 717–719 (2003)Google Scholar
  8. 8.
    Fagin, R.: Fuzzy Queries in Multimedia Database Systems. In: PODS, pp. 1–10 (1998)Google Scholar
  9. 9.
    Godfrey, P., Shipley, R., Gryz, J.: Maximal Vector Computation in Large Data Sets. In: VLDB, pp. 229–240 (2005)Google Scholar
  10. 10.
    Hjaltason, G.R., Samet, H.: Distance Browsing in Spatial Databases. ACM TODS 24, 265–318 (1999)CrossRefGoogle Scholar
  11. 11.
    Hristidis, V., Koudas, N., Papakonstantinou, Y.: PREFER: A System for the Efficient Execution of Multi-parametric Ranked Queries. In: SIGMOD, pp. 259–270 (2001)Google Scholar
  12. 12.
    Kossmann, D., Ramsak, F., Rost, S.: Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. In: VLDB, pp. 275–286 (2002)Google Scholar
  13. 13.
    Lin, X., Yuan, Y., Wang, W., Lu, H.: Stabbing the Sky: Efficient Skyline Computation over Sliding Windows. In: ICDE, pp. 502–513 (2005)Google Scholar
  14. 14.
    Natsev, A., Chang, Y.-C., Smith, J.R., Li., C.-S., Vitter, J.S.: Supporting Incremental Join Queries on Ranked Inputs. In: VLDB, pp. 281–290 (2001)Google Scholar
  15. 15.
    Papadias, D., Tao, Y., Greg, F., Seeger, B.: Progressive Skyline Computation in Database Systems. ACM TODS 30, 41–82 (2005)CrossRefGoogle Scholar
  16. 16.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the Best Views of Skyline: A Semantic Approach Based on Decisive Subspaces. In: VLDB, pp. 253–264 (2005)Google Scholar
  17. 17.
    Preparata, F., Shamos, M.: Computational Geometry: An Introduction. Springer, Heidelberg (1985)Google Scholar
  18. 18.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)Google Scholar
  19. 19.
    Tan, K.-L., Eng, P.-K., Ooi, B.C.: Efficient Progressive Skyline Computation. In: VLDB, pp. 301–310 (2001)Google Scholar
  20. 20.
    Theodoridis, Y., Sellis, T.K.: A Model for the Prediction of R-tree Performance. In: PODS, pp. 161–171 (1996)Google Scholar
  21. 21.
    Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient Computation of the Skyline Cube. In: VLDB, pp. 241–252 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yunjun Gao
    • 1
  • Gencai Chen
    • 1
  • Ling Chen
    • 1
  • Chun Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China

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