Efficient Policy-Based Inconsistency Management in Relational Knowledge Bases

  • Maria Vanina Martinez
  • Francesco Parisi
  • Andrea Pugliese
  • Gerardo I. Simari
  • V. S. Subrahmanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6379)

Abstract

Real-world databases are frequently inconsistent. Even though the users who work with a body of data are far more familiar not only with that data, but also their own job and the risks they are willing to take and the inferences they are willing to make from inconsistent data, most DBMSs force them to use the policy embedded in the DBMS. Inconsistency management policies (IMPs) were introduced so that users can apply policies that they deem are appropriate for data they know and understand better than anyone else. In this paper, we develop an efficient “cluster table” method to implement IMPs and show that using cluster tables instead of a standard DBMS index is far more efficient when less than about 3% of a table is involved in an inconsistency (which is hopefully the case in most real world DBs), while standard DBMS indexes perform better when the amount of inconsistency in a database is over 3%.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS, pp. 68–79 (1999)Google Scholar
  2. 2.
    Arenas, M., Bertossi, L.E., Chomicki, J.: Answer sets for consistent query answering in inconsistent databases. TPLP 3(4-5), 393–424 (2003)MATHMathSciNetGoogle Scholar
  3. 3.
    Baral, C., Kraus, S., Minker, J.: Combining multiple knowledge bases. TKDE 3(2), 208–220 (1991)Google Scholar
  4. 4.
    Belnap, N.: A useful four valued logic. Modern Uses of Many Valued Logic, 8–37 (1977)Google Scholar
  5. 5.
    Bertossi, L.E., Bravo, L., Franconi, E., Lopatenko, A.: The complexity and approximation of fixing numerical attributes in databases under integrity constraints. Information Systems 33(4-5), 407–434 (2008)CrossRefGoogle Scholar
  6. 6.
    Besnard, P.: Remedying inconsistent sets of premises. Int. J. Approx. Reasoning 45(2), 308–320 (2007)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Blair, H.A., Subrahmanian, V.S.: Paraconsistent logic programming. Theor. Comp. Sci. 68(2), 135–154 (1989)Google Scholar
  8. 8.
    Bohannon, P., Fan, W., Flaster, M., Rastogi, R.: A cost-based model and effective heuristic for repairing constraints by value modification. In: SIGMOD 2005, pp. 143–154 (2005)Google Scholar
  9. 9.
    Calì, A., Lembo, D., Rosati, R.: On the decidability and complexity of query answering over inconsistent and incomplete databases. In: PODS, pp. 260–271 (2003)Google Scholar
  10. 10.
    Caniupán Marileo, M., Bertossi, L.E.: The consistency extractor system: Answer set programs for consistent query answering in databases. Data Knowl. Eng. 69(6), 545–572 (2010)CrossRefGoogle Scholar
  11. 11.
    Chomicki, J.: Consistent query answering: Five easy pieces. In: Schwentick, T., Suciu, D. (eds.) ICDT 2007. LNCS, vol. 4353, pp. 1–17. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Chomicki, J., Marcinkowski, J.: Minimal-change integrity maintenance using tuple deletions. Inf. Comp. 197(1-2), 90–121 (2005)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Chomicki, J., Marcinkowski, J., Staworko, S.: Computing consistent query answers using conflict hypergraphs. In: Proc. 13th ACM Conf. on Information and Knowledge Management (CIKM), pp. 417–426 (2004)Google Scholar
  14. 14.
    da Costa, N.: On the theory of inconsistent formal systems. N. Dame J. of Formal Logic 15(4), 497–510 (1974)MATHCrossRefGoogle Scholar
  15. 15.
    de Saint-Cyr, F.D., Prade, H.: Handling uncertainty and defeasibility in a possibilistic logic setting. Int. J. Approx. Reasoning 49(1), 67–82 (2008)MATHCrossRefGoogle Scholar
  16. 16.
    Fitting, M.: Bilattices and the semantics of logic programming. J. of Log. Prog. 11(1-2), 91–116 (1991)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Flesca, S., Furfaro, F., Parisi, F.: Querying and repairing inconsistent numerical databases. ACM Trans. Database Syst. 35 (2) (2010)Google Scholar
  18. 18.
    Franconi, E., Palma, A.L., Leone, N., Perri, S., Scarcello, F.: Census data repair: a challenging application of disjunctive logic programming. In: LPAR, pp. 561–578 (2001)Google Scholar
  19. 19.
    Fuxman, A., Miller, R.J.: First-order query rewriting for inconsistent databases. J. Comput. Syst. Sci. 73(4), 610–635 (2007)MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Grant, J., Hunter, A.: Measuring inconsistency in knowledgebases. J. of Intel. Inf. Syst. 27(2), 159–184 (2006)CrossRefGoogle Scholar
  21. 21.
    Greco, G., Greco, S., Zumpano, E.: A logical framework for querying and repairing inconsistent databases. IEEE TKDE 15(6), 1389–1408 (2003)Google Scholar
  22. 22.
    Hunter, A., Konieczny, S.: Approaches to measuring inconsistent information. In: Bertossi, L., Hunter, A., Schaub, T. (eds.) Inconsistency Tolerance. LNCS, vol. 3300, pp. 191–236. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Jermyn, P., Dixon, M., Read, B.J.: Preparing clean views of data for data mining. In: ERCIM Work. on Database Res., pp. 1–15 (1999)Google Scholar
  24. 24.
    Kifer, M., Lozinskii, E.L.: A logic for reasoning with inconsistency. J. of Autom. Reas. 9(2), 179–215 (1992)MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Lozinskii, E.L.: Resolving contradictions: A plausible semantics for inconsistent systems. J. of Autom. Reas. 12(1), 1–31 (1994)MATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Martinez, M.V., Parisi, F., Pugliese, A., Simari, G.I., Subrahmanian, V.S.: Inconsistency management policies. In: KR, pp. 367–377 (2008)Google Scholar
  27. 27.
    Reiter, R.: A logic for default reasoning. Artif. Intel. 13(1-2), 81–132 (1980)MATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Staworko, S., Chomicki, J.: Consistent query answers in the presence of universal constraints. Inf. Syst. 35(1), 1–22 (2010)CrossRefGoogle Scholar
  29. 29.
    Subrahmanian, V.S., Amgoud, L.: A general framework for reasoning about inconsistency. In: IJCAI, pp. 599–504 (2007)Google Scholar
  30. 30.
    Touretzky, D.: The mathematics of inheritance systems. Morgan Kaufmann, San Francisco (1986)MATHGoogle Scholar
  31. 31.
    Wijsen, J.: Database repairing using updates. ACM TODS 30(3), 722–768 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Maria Vanina Martinez
    • 1
  • Francesco Parisi
    • 2
  • Andrea Pugliese
    • 2
  • Gerardo I. Simari
    • 1
  • V. S. Subrahmanian
    • 1
  1. 1.Department of Computer Science and UMIACSUniversity of Maryland College ParkCollege ParkUSA
  2. 2.Università della CalabriaRendeItaly

Personalised recommendations