Query Optimization Strategies in Similarity-Based Databases

  • Petr Krajca
  • Vilem Vychodil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8234)

Abstract

We deal with algorithmic aspects and implementation issues of query execution in relational similarity-based databases. We are concerned with a generalized relational model of data in which queries can be matched to degrees taken from scales represented by complete residuated lattices. The main contribution of this paper are optimization techniques for efficient evaluation of queries involving similarity-based restrictions. In addition, we present experimental evaluation of the proposed techniques showing their efficiency compared to naive approaches.

Keywords

domain similarities fuzzy logic monotone queries query execution relational model of data residuated lattices 

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References

  1. 1.
    Belohlavek, R.: Fuzzy Relational Systems: Foundations and Principles. Kluwer Academic Publishers, Norwell (2002)CrossRefGoogle Scholar
  2. 2.
    Belohlavek, R., Opichal, S., Vychodil, V.: Relational algebra for ranked tables with similarities: Properties and implementation. In: Berthold, M.R., Shawe-Taylor, J., Lavrac, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 140–151. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Bělohlávek, R., Vychodil, V.: Data tables with similarity relations: Functional dependencies, complete rules and non-redundant bases. In: Li Lee, M., Tan, K.-L., Wuwongse, V. (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 644–658. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Belohlavek, R., Vychodil, V.: Query systems in similarity-based databases: logical foundations, expressive power, and completeness. In: ACM Symposium on Applied Computing (SAC), pp. 1648–1655. ACM (2010)Google Scholar
  5. 5.
    Buckles, B.P., Petry, F.E.: A fuzzy representation of data for relational databases. Fuzzy Sets and Systems 7(3), 213–226 (1982)CrossRefMATHGoogle Scholar
  6. 6.
    Cavallo, R., Pittarelli, M.: The theory of probabilistic databases. In: Proceedings of the 13th International Conference on Very Large Data Bases, VLDB 1987, pp. 71–81. Morgan Kaufmann Publishers Inc., San Francisco (1987)Google Scholar
  7. 7.
    Cintula, P., Hájek, P.: Triangular norm based predicate fuzzy logics. Fuzzy Sets and Systems 161, 311–346 (2010)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Codd, E.F.: A relational model of data for large shared data banks. Communications of the ACM 26, 64–69 (1983)CrossRefGoogle Scholar
  9. 9.
    Dalvi, N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52, 86–94 (2009)CrossRefGoogle Scholar
  10. 10.
    Dalvi, N., Suciu, D.: Efficient query evaluation on probabilistic databases. The VLDB Journal 16, 523–544 (2007)CrossRefGoogle Scholar
  11. 11.
    Dalvi, N., Suciu, D.: Management of probabilistic data: foundations and challenges. In: Proc. ACM PODS 2007, pp. 1–12. ACM, New York (2007)Google Scholar
  12. 12.
    Date, C.J., Darwen, H.: Databases, Types, and The Relational Model: The Third Manifesto, 3rd edn. Addison-Wesley (2006)Google Scholar
  13. 13.
    Date, C.J.: Database in Depth: Relational Theory for Practitioners: The Relational Model for Practitioners, 1st edn. O’Reilly Media (2005)Google Scholar
  14. 14.
    Esteva, F., Godo, L.: Monoidal t-norm based logic: towards a logic for left-continuous t-norms. Fuzzy Sets and Systems 124(3), 271–288 (2001)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Fagin, R.: Combining fuzzy information from multiple systems. J. Comput. Syst. Sci. 58(1), 83–99 (1999)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Goguen, J.A.: The logic of inexact concepts. Synthese 19, 325–373 (1979)CrossRefGoogle Scholar
  17. 17.
    Gottwald, S.: Mathematical fuzzy logics. Bull. Symb. Logic 14(2), 210–239 (2008)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Gupta, R., Sarawagi, S.: Creating probabilistic databases from information extraction models. In: Proceedings of the 32nd International Conference on Very large Data Bases, VLDB 2006, pp. 965–976. VLDB Endowment (2006)Google Scholar
  19. 19.
    Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer Academic Publishers, Dordrecht (1998)CrossRefMATHGoogle Scholar
  20. 20.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Comp. Surv. 40(4), 11:1–11:58 (2008)Google Scholar
  21. 21.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms, 1st edn. Springer (2000)Google Scholar
  22. 22.
    Krajca, P., Vychodil, V.: Foundations of relational similarity-based query language RESIQL. In: Proc. 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI), pp. 15–23. IEEE (2013)Google Scholar
  23. 23.
    Li, C., Chang, K.C.C., Ilyas, I.F., Song, S.: Ranksql: query algebra and optimization for relational top-k queries. In: Proc. 2005 ACM SIGMOD, pp. 131–142 (2005)Google Scholar
  24. 24.
    Maier, D.: The Theory of Relational Databases. Computer Science Press (1983)Google Scholar
  25. 25.
    Prade, H., Testemale, C.: Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries. Information Sciences 34(2), 115–143 (1984)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Petr Krajca
    • 1
  • Vilem Vychodil
    • 1
  1. 1.DAMOL (Data Analysis and Modeling Laboratory), Dept. Computer SciencePalacky University, OlomoucOlomoucCzech Republic

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