On Skyline Queries and How to Choose from Pareto Sets

  • Christoph Lofi
  • Wolf-Tilo Balke
Part of the Intelligent Systems Reference Library book series (ISRL, volume 36)


Skyline queries are well known for their intuitive query formalization and easy to understand semantics when selecting the most interesting database objects in a personalized fashion. They naturally fill the gap between set-based SQL queries and rank-aware database retrieval and thus have emerged in the last few years as a popular tool for personalized retrieval in the database research community. Unfortunately, the Skyline paradigm also exhibits some significant drawbacks. Most prevalent among those problems is the so called “curse of dimensionality” which often leads to unmanageable result set sizes. This flood of query results, usually containing a significant portion of the original database, in turn severely hampers the paradigm’s applicability in real-life systems. In this chapter, we will provide a survey of techniques to remedy this problem by choosing the most interesting objects from the multitude of skyline objects in order to obtain truly manageable and personalized query results.


Skyline Query Skyline Point Subspace Analysis Very Large Data Base 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline Operator. In: Int. Conf. on Data Engineering (ICDE), Heidelberg, Germany (2001)Google Scholar
  2. 2.
    Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: Symposium on Principles of Database Systems (PODS), Santa-Barbara, California, USA (2001)Google Scholar
  3. 3.
    Kossmann, D., Ramsak, F., Rost, S.: Shooting Stars in the Sky: an Online Algorithm for Skyline Queries. In: Int. Conf. on Very Large Data Bases, VLDB, Hongkong, China (2002)Google Scholar
  4. 4.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: An Optimal and Progressive Algorithm for Skyline Queries. In: International Conference on Management of Data (SIGMOD), San Diego, USA (2003)Google Scholar
  5. 5.
    Lacroix, M., Lavency, P.: Preferences: Putting More Knowledge into Queries. In: Int. Conf. on Very Large Data Bases (VLDB), Brighton, UK (1987)Google Scholar
  6. 6.
    Chan, C.-Y., Eng, P.-K., Tan, K.-L.: Stratified Computation of Skylines with Partially-Ordered Domains. In: International Conference on Management of Data (SIGMOD), Baltimore, USA (2005)Google Scholar
  7. 7.
    Bentley, J.L., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the Average Number of Maxima in a Set of Vectors and Applications. Journal of the ACM (JACM) 25 (1978)Google Scholar
  8. 8.
    Chaudhuri, S., Dalvi, N., Kaushik, R.: Robust Cardinality and Cost Estimation for Skyline Operator. In: 22nd Int. Conf. on Data Engineering (ICDE), Atlanta, Georgia, USA (2006)Google Scholar
  9. 9.
    Godfrey, P.: Skyline Cardinality for Relational Processing. In: Seipel, D., Turull-Torres, J.M. (eds.) FoIKS 2004. LNCS, vol. 2942, pp. 78–97. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Balke, W.-T., Zheng, J.X., Güntzer, U.: Approaching the Efficient Frontier: Cooperative Database Retrieval Using High-Dimensional Skylines. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 410–421. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Hansson, S.O.: Preference Logic. In: Handbook of Philosophical Logic, vol. 4, pp. 319–393 (2002)Google Scholar
  12. 12.
    Godfrey, P., Shipley, R., Gryz, J.: Algorithms and Analyses for Maximal Vector Computation. The VLDB Journal 16, 5–28 (2007)CrossRefGoogle Scholar
  13. 13.
    Eng, P.-K., Ooi, B.C., Tan, K.-L.: Indexing for Progressive Skyline Computation. Data 4 Knowledge Engineering 46, 169–201 (2003)CrossRefGoogle Scholar
  14. 14.
    Godfrey, P., Gryz, J., Liang, D., Chomicki, J.: Skyline with Presorting. In: 19th International Conference on Data Engineering (ICDE), Bangalore, India (2003)Google Scholar
  15. 15.
    Papadias, D., Tao, G.F.Y., Seeger, B.: Progressive Skyline Computation in Database Systems. ACM Transactions on Database Systems 30, 41–82 (2005)CrossRefGoogle Scholar
  16. 16.
    Ciaccia, P., Patella, M., Bartolini, I.: Efficient Sort-Based Skyline Evaluation. ACM Transactions on Database Systems 33 (2008)Google Scholar
  17. 17.
    Torlone, R., Ciaccia, P.: Finding the Best When It’s a Matter of Preference. In: 10th Italian Symposium on Advanced Database Systems (SEBD), Portoferraio, Italy (2002)Google Scholar
  18. 18.
    Boldi, P., Chierichetti, F., Vigna, S.: Pictures from Mongolia: Extracting the Top Elements from a Partially Ordered Set. Theory of Computing Systems 44, 269–288 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Kim, T., Park, J., Kim, J., Im, H., Park, S.: Parallel Skyline Computation on Multicore Architectures. In: 25th International Conference on Data Engineering (ICDE), Shanghai, China (2009)Google Scholar
  20. 20.
    Fishburn, P.: Preference Structures and their Numerical Representations. Theoretical Computer Science 217, 359–383 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Kießling, W.: Foundations of Preferences in Database Systems. In: 28th Int. Conf. on Very Large Data Bases (VLDB), Hong Kong, China (2002)Google Scholar
  22. 22.
    Chomicki, J.: Querying with Intrinsic Preferences. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 34–51. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  23. 23.
    Balke, W.T., Güntzer, U., Siberski, W.: Restricting Skyline Sizes Using Weak Pareto Dominance. Informatik - Forschung und Entwicklung 21, 165–178 (2007)CrossRefGoogle Scholar
  24. 24.
    Kießling, W.: Preference Queries with SV-Semantics. In: 11th Int. Conf. On Management of Data (COMAD 2005), Goa, India (2005)Google Scholar
  25. 25.
    Chan, C.-Y.: Finding k-Dominant Skylines in High Dimensional Space. In: ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2006), Chicago, Illinois, USA (2006)Google Scholar
  26. 26.
    Koltun, V., Papadimitriou, C.: Approximately Dominating Representatives. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 204–214. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Yuan, Y.: Efficient Computation of the Skyline Cube. In: 31st Int. Conf. on Very Large Databases (VLDB 2005), Trondheim, Norway (2005)Google Scholar
  28. 28.
    Pei, J.: Catching the Best Views of Skyline: a Semantic Approach Based on Decisive Subspaces. In: 31st Int. Conf. on Very Large Databases (VLDB 2005), Trondheim, Norway (2005)Google Scholar
  29. 29.
    Pei, J.: Towards Multidimensional Subspace Skyline Analysis. ACM Transactions on Database Systems (TODS) 31, 1335–1381 (2006)CrossRefGoogle Scholar
  30. 30.
    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
  31. 31.
    Vlachou, A., Vazirgiannis, M.: Ranking the Sky: Discovering the Importance of Skyline Points through Subspace Dominance Relationships. Data & Knowledge Engineering 69, 943–964 (2010)CrossRefGoogle Scholar
  32. 32.
    Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30, 107–117 (1998)CrossRefGoogle Scholar
  33. 33.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting Stars: The k Most Representative Skyline Operator. In: 23rd IEEE International Conference on Data Engineering, Istanbul, Turkey (2007)Google Scholar
  34. 34.
    Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-Based Representative Skyline. In: 25th Int. Conf. on Data Engineering (ICDE), Shanghai, China (2009)Google Scholar
  35. 35.
    Lee, J., You, G.-W., Hwang, S.-W.: Personalized Top-k Skyline Queries in High-Dimensional Space. Information Systems 34, 45–61 (2009)zbMATHCrossRefGoogle Scholar
  36. 36.
    Lee, J., You, G.-W., Hwang, S.-W., Selke, J., Balke, W.-T.: Optimal Preference Elicitation for Skyline Queries over Categorical Domains. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 610–624. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  37. 37.
    Lofi, C., Güntzer, U., Balke, W.-T.: Efficient Computation of Trade-Off Skylines. In: 13th International Conference on Extending Database Technology (EDBT), Lausanne, Switzerland (2010)Google Scholar
  38. 38.
    Balke, W.-T., Lofi, C., Güntzer, U.: Incremental Trade-Off Management for Preference Based Queries. International Journal of Computer Science & Applications (IJCSA) 4, 75–91 (2007)Google Scholar
  39. 39.
    Balke, W.-T., Güntzer, U., Lofi, C.: Eliciting Matters – Controlling Skyline Sizes by Incremental Integration of User Preferences. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 551–562. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  40. 40.
    Lofi, C., Balke, W.-T., Güntzer, U.: Consistency Check Algorithms for Multi-Dimensional Preference Trade-Offs. International Journal of Computer Science & Applications (IJCSA) 5, 165–185 (2008)Google Scholar
  41. 41.
    Lofi, C., Balke, W.-T., Güntzer, U.: Efficient Skyline Refinement Using Trade-Offs Respecting Don’t-Care Attributes. International Journal of Computer Science and Applications (IJCSA) 6, 1–29 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Technische Universität BraunschweigBraunschweigGermany

Personalised recommendations