Evaluating Top-k Skyline Queries over Relational Databases

  • Carmen Brando
  • Marlene Goncalves
  • Vanessa González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)


Two main languages have been defined to allow users to express their preference criteria: Top-k and Skyline. Top-k ranks the top k tuples in terms of a user-defined score function while Skyline identifies non-dominated tuples, i.e. such tuples that does not exists a better one in all user criteria. A third language, Top-k Skyline, integrates them. One of the drawbacks of relational engines is that they do not understand the notion of preferences. However, some solutions for Skyline and Top-k queries have been integrated into relational engines. The solutions implemented outside the core query engine have lost the advantages of true integration with other basic database query types. To the best of our knowledge, none of the existing engines supports Top-k Skyline queries. In this work, we propose two evaluation algorithms for Top-k Skyline which were implemented in PostgreSQL, and we report initial experimental results that show their properties.


Score Function Skyline Query Skyline Algorithm Preference Query Initial Experimental Result 
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.
    Balke, W-T., Güntzer, U., Kiebling, W.: Towards Efficient Multi-Feature Queries in Heterogeneous Environments. In: Proceedings of the IEEE International Conference on Information Technology: Coding and Computing (ITCC), pp. 622–628 (April 2001)Google Scholar
  2. 2.
    Balke, W-T., Güntzer, U., Zheng, J.: 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)Google Scholar
  3. 3.
    Balke, W-T., Güntzer, U.: Multi-Objective Query Processing for Database Systems. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 936–947 (September 2004)Google Scholar
  4. 4.
    Bruno, N., Gravano, L., Marian, A.: Evaluating Top-k Queries over Web-Accessible Databases. In: Proceedings of International Conference on Data Engineering (ICDE), vol. 29(4), pp. 319–362 (2002)Google Scholar
  5. 5.
    Carey, M., Kossman, D.: On saying Enough Already! in SQL. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 219–230 (May 1997)Google Scholar
  6. 6.
    Carey, M., Kossman, D.: Reducing the Braking Distance of a SQL Query Engine. In: Proceedings of VLDB, pp. 158–169 (August 1998)Google Scholar
  7. 7.
    Chang, K., Hwang, S-W.: Optimizing Access Cost for Top-k Queries over Web Sources: A Unified Cost-Based Approach. Technical Report UIUCDS-R-2003-2324, University of Illinois at Urbana-Champaign (March 2003)Google Scholar
  8. 8.
    Fagin, R.: Combining Fuzzy Information from Multiple Systems. Journal of Computer and System Sciences (JCSS) 58(1), 216–226 (1996)MathSciNetGoogle Scholar
  9. 9.
    Godfrey, P., Shipley, R., Gryz, J.: Maximal Vector Computation in Large Data Sets. In: Proceedings of VLDB, pp. 229–240 (2005)Google Scholar
  10. 10.
    Goncalves, M., Vidal, M.E.: Preferred Skyline: A Hybrid Approach Between SQLf and Skyline. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 375–384. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Goncalves, M., Vidal, M.E.: Top-k Skyline: A Unified Approach. In: Proceedings of OTM (On the Move) 2005 PhD Symposium, pp. 790–799 (2005)Google Scholar
  12. 12.
    Huang, Z., Jensen, C.S., Lu, H., Ooi, B.C.: Skyline Queries Against Mobile Lightweight Devices in MANETs. In: Proceedings of ICDE, pp. 66–77 (2006)Google Scholar
  13. 13.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting Top-k Join Queries in Relational Databases. In: Proceedings of VLDB, pp. 754–765 (2003)Google Scholar
  14. 14.
    Kossman, D., Ransak, F., Rost, S.: Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. In: Proceedings of VLDB, pp. 275–286 (2002)Google Scholar
  15. 15.
    Lo, E., Yip, K., Lin, K-I., Cheung, D.: Progressive Skylining over Web-Accessible Databases. Journal of Data and Knowledge Engineering 57(2), 122–147 (2006)CrossRefGoogle Scholar
  16. 16.
    Natsev, A., Chang, Y-CH., Smith, J.R., Li, CH.-S., Vitter, J.S.: Supporting Incremental Join Queries on Ranked Inputs. In: Proceedings of VLDB, pp. 281–290 (2001)Google Scholar
  17. 17.
    Nepal, S., Ramakrishnan, M.V.: Query Processing Issues in Image (Multimedia) Databases. In: Proceedings of ICDE, pp. 22–29 (1999)Google Scholar
  18. 18.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive Skyline Computation in Database Systems. ACM Transactions Database Systems 30(1), 41–82 (2005)CrossRefGoogle Scholar
  19. 19.
    Tan, K-L., Eng, P-K., Ooi, B.C.: Efficient Progressive Skyline Computation. In: Proceedings of VLDB, pp. 301–310 (2001)Google Scholar
  20. 20.
    Zagat Survey Guides: available at

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Carmen Brando
    • 1
  • Marlene Goncalves
    • 1
  • Vanessa González
    • 1
  1. 1.Universidad Simón Bolívar, Departamento de Computación y TI, Apartado 89000, Caracas 1080-AVenezuela

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