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A Formal-Concept-Lattice Driven Approach for Skyline Refinement

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Skyline queries constitute an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in sense of Pareto) objects from a set of data. However, this process often leads to a huge skyline, which is less informative for the end-users. In this paper, we propose an efficient approach to refine the skyline and reduce its size, using the principle of the formal concepts analysis. The basic idea is to build a formal concept lattice for skyline objects based on the minimal distance between each concept and the target concept. We show that the refined skyline is given by the concept that contains k objects (where k is a user-defined parameter) and has the minimal distance to the target concept. A set of experiments are conducted to demonstrate the effectiveness and efficiency of our approach.

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References

  1. Abbaci, K., Hadjali, A., Lietard, L., Rocacher, D.: A linguistic quantifier-based approach for skyline refinement. In: Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS, 24–28 June, Edmonton, Alberta, Canada, pp. 321–326 (2013)

    Google Scholar 

  2. Balke, W., Guntzer, U., Lofi, C.: User interaction support for incremental refinement of preference-based queries. In: Proceedings of the First Inter. Conference on Research Challenges in Information Science (RCIS), 23–26 April, Ouarzazate, Morocco, pp. 209–220 (2007)

    Google Scholar 

  3. Belohlávek, R.: Fuzzy galois connections. Math. Log. Q. 45, 497–504 (1999)

    Google Scholar 

  4. B\(\ddot{o}\)rzs\(\ddot{o}\)nyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, 2–6 April, Heidelberg, Germany, pp. 421–430 (2001)

    Google Scholar 

  5. Burusco, A., Fuentes-González, R.: The study of the l-fuzzy concept lattice. Mathware Soft Comput. 3(1), 208–209 (1994)

    MathSciNet  MATH  Google Scholar 

  6. Chan, C.Y., Jagadish, H.V., Tan, K., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: Proceedings of the International Conference on Management of Data (ACM SIGMOD), 27–29 June, Chicago, Illinois, USA, pp. 503–514 (2006)

    Google Scholar 

  7. Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On high dimensional skylines. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 478–495. Springer, Heidelberg (2006). https://doi.org/10.1007/11687238_30

    Chapter  Google Scholar 

  8. Chomicki, J., Ciaccia, P., Meneghetti, N.: Skyline queries, front and back. SIGMOD Rec. 42(3), 6–18 (2013)

    Article  Google Scholar 

  9. Elmi, S., Tobji, M.A.B., Hadjali, A., Yaghlane, B.B.: Selecting skyline stars over uncertain databases: semantics and refining methods in the evidence theory setting. Appl. Soft Comput. 57, 88–101 (2017)

    Article  Google Scholar 

  10. Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  MATH  Google Scholar 

  11. Goncalves, M., Tineo, L.: Fuzzy dominance skyline queries. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 469–478. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74469-6_46

    Chapter  Google Scholar 

  12. Gulzar, Y., Alwan, A.A., Salleh, N., Shaikhli, I.F.A.: Processing skyline queries in incomplete database: issues, challenges and future trends. JCS 13(11), 647–658 (2017)

    Google Scholar 

  13. Haddache, M., Belkasmi, D., Hadjali, A., Azzoune, H.: An outranking-based approach for skyline refinement. In: 8th IEEE International Conference on Intelligent Systems, IS 2016, 4–6 September 2016, Sofia, Bulgaria, pp. 333–344 (2016)

    Google Scholar 

  14. Hadjali, A., Pivert, O., Prade, H.: Possibilistic contextual skylines with incomplete preferences. In: Second International Conference of Soft Computing and Pattern Recognition, (SoCPaR), 7–10 December, Cergy Pontoise/Paris, France, pp. 57–62 (2010)

    Google Scholar 

  15. Hadjali, A., Pivert, O., Prade, H.: On different types of fuzzy skylines. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 581–591. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21916-0_62

    Chapter  Google Scholar 

  16. Hamiche, M., Hadjali, A., Drias, H.: A strong-dominance-based approach for refining the skyline. In: Proceedings of the 12th International Symposium on Programming and Systems (ISPS), 28–30 April, Algiers, Algeria, pp. 1–8 (2015)

    Google Scholar 

  17. Khalefa, M.E., Mokbel, M.F., Levandoski, J.J.: Skyline query processing for incomplete data. In: Proceedings of the 24th International Conference on Data Engineering, ICDE 2008, 7–12 April 2008, Canc\(\acute{u}\)n, M\(\acute{e}\)xico, pp. 555–565 (2008)

    Google Scholar 

  18. Koltun, V., Papadimitriou, C.H.: Approximately dominating representatives. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 204–214. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30570-5_14

    Chapter  Google Scholar 

  19. Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), 20–23 August, Hong Kong, China, pp. 275–286 (2002)

    Google Scholar 

  20. Lee, J., Hwang, S.: Scalable skyline computation using a balanced pivot selection technique. Inf. Syst. 39, 1–21 (2014)

    Article  Google Scholar 

  21. Lee, J., You, G., Hwang, S.: Telescope: zooming to interesting skylines. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 539–550. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71703-4_46

    Chapter  Google Scholar 

  22. Loyer, Y., Sadoun, I., Zeitouni, K.: Personalized progressive filtering of skyline queries in high dimensional spaces. In: Proceedings of the 17th International Conference on Database Engineering Applications Symposium (IDEAS), 09–11 October, Barcelona, Spain, pp. 186–191 (2013)

    Google Scholar 

  23. Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceedings of the International Conference on Management of Data (ACM SIGMOD), 9–12 June, San Diego, California, USA, pp. 467–478 (2003)

    Google Scholar 

  24. Pei, J., Jiang, B., Lin, X., Yuan, Y.: Probabilistic skylines on uncertain data. In: Proceedings of the 33rd International Conference on Very Large Data Bases, University of Vienna, 23–27 September, Austria, pp. 15–6 (2007)

    Google Scholar 

  25. Santiago, A., et al.: A survey of decomposition methods for multi-objective optimization. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds.) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. SCI, vol. 547, pp. 453–465. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05170-3_31

    Chapter  Google Scholar 

  26. Sarma, A.D., Lall, A., Nanongkai, D., Lipton, R.J., Xu, J.J.: Representative skylines using threshold-based preference distributions. In: Proceedings of the 27th International Conference on Data Engineering, (ICDE), 11–16 April, Hannover, Germany, pp. 387–398 (2011)

    Google Scholar 

  27. Tan, K., Eng, P., Ooi, B.C.: Efficient progressive skyline computation. In: Proceedings of 27th International Conference on Very Large Data Bases (VLDB), 11–14 September, Roma, Italy (2001)

    Google Scholar 

  28. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Springer, Heidelberg (1982). https://doi.org/10.1007/978-94-009-7798-3_15

    Chapter  Google Scholar 

  29. Yiu, M.L., Mamoulis, N.: Efficient processing of top-k dominating queries on multi-dimensional data. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB), 23–27 September, University of Vienna, Austria, pp. 483–494 (2007)

    Google Scholar 

  30. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. DIF-FS/UMBB, Boumerdes, Algeria

    Mohamed Haddache

  2. LIAS/ENSMA, Poitiers, France

    Allel Hadjali

  3. LRIA/USTHB, Algers, Algeria

    Hamid Azzoune

Authors
  1. Mohamed Haddache
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  2. Allel Hadjali
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  3. Hamid Azzoune
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Corresponding authors

Correspondence to Mohamed Haddache , Allel Hadjali or Hamid Azzoune .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Haddache, M., Hadjali, A., Azzoune, H. (2019). A Formal-Concept-Lattice Driven Approach for Skyline Refinement. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_47

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

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