Distributed and Parallel Databases

, Volume 26, Issue 1, pp 127–150

Ranking strategies and threats: a cost-based pareto optimization approach

Article
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Abstract

Skyline queries have gained attention as an effective way to identify desirable objects that are “not dominated” by another object in the dataset. From market perspective, such objects are favored as pareto-optimal choices, as each of such objects has at least one competitive edge against all other objects, or not dominated. In other words, non-skyline objects have room for pareto-optimal improvements for more favorable positioning in the market. The goal of this paper is, for such non-skyline objects, to identify the cost-minimal pareto-optimal improvement strategy. More specifically, we abstract this problem as a mixed integer programming problem and develop a novel algorithm for efficiently identifying the optimal solution. In addition, the problem can be reversed to identify, for a skyline product, top-k threats that can be competitors after pareto-optimal improvements with the k lowest costs. Through extensive experiments using synthetic and real-life datasets, we show that our proposed framework is both efficient and scalable.

Keywords

Preference Linear programming MIP Pareto-optimal 

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References

  1. 1.
    Bentley, J.L., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. J. ACM 25, 536–543 (1978) MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proc. of ICDE, pp. 421–430, 2001 Google Scholar
  3. 3.
    Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Proc. of ICDE, pp. 717–719, 2003 Google Scholar
  4. 4.
    Feldman, A.M.: Welfare Economics and Social Choice Theory, 2nd edn. Springer, Berlin (2006) Google Scholar
  5. 5.
    Hiller, F.S., Lieberman, G.J.: Introduction to Operations Research, 8th edn. McGraw-Hill, New York (2005) Google Scholar
  6. 6.
    Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24, 265–318 (1999) CrossRefGoogle Scholar
  7. 7.
    Kim, Y., You, G.w., Hwang, S.w.: Escaping a dominance region at minimum cost. In: Proc. of DEXA, pp. 800–807, 2008 Google Scholar
  8. 8.
    Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: Proc. of VLDB, pp. 275–286, 2002 Google Scholar
  9. 9.
    Li, C., Tung, A.K.H., Jin, W., Ester, M.: On dominating your neighborhood profitably. In: Proc. of VLDB, pp. 818–829, 2007 Google Scholar
  10. 10.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proc. of SIGMOD, pp. 71–79, 1995 Google Scholar
  11. 11.
    Zou, L., Chen, L.: Dominant graph: An efficient indexing structure to answer top-k queries. In: Proc. of ICDE, pp. 536–545, 2008 Google Scholar
  12. 12.
    ILOG CPLEX 9.0 user’s manual. http://www.ilog.com/products/cplex

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Pohang University of Science and TechnologyPohangRepublic of Korea

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