Feasibility Conditions and Preference Criteria in Querying and Repairing Inconsistent Databases

  • Sergio Greco
  • Cristina Sirangelo
  • Irina Trubitsyna
  • Ester Zumpano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3180)


Recently there has been an increasing interest in integrity constraints associated with relational databases and in inconsistent databases, i.e. databases which do not satisfy integrity constraints. In the presence of inconsistencies two main techniques have been proposed: compute repairs, i.e. minimal set of insertion and deletion operations, called database repairs, and compute consistent answers, i.e. identify the sets of atoms which we can assume true, false and undefined without modifying the database. In this paper feasibility conditions and preference criteria are introduced which, associated with integrity constraints, allow to restrict the number of repairs and to increase the power of queries over inconsistent databases. Moreover, it is studied the complexity of computing repairs and the expressive power of relational queries over databases with integrity constraints, feasibility conditions and preference criteria.


Expressive Power Search Query Integrity Constraint Optimization Query Feasibility Condition 
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.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1994)Google Scholar
  2. 2.
    Argaval, S., Keller, A.M., Wiederhold, G., Saraswat, K.: Flexible Relation: an Approach for Integrating Data from Multiple, Possibly Inconsistent Databases. In: ICDE (1995)Google Scholar
  3. 3.
    Arenas, M., Bertossi, L., Chomicki, J.: Consistent Query Answers in Inconsistent Databases. In: PODS, pp. 68–79 (1999)Google Scholar
  4. 4.
    Arenas, M., Bertossi, L., Chomicki, J.: Specifying and Querying Database repairs using Logic Programs with Exceptions. In: FQAS, pp. 27–41 (2000)Google Scholar
  5. 5.
    Brewka, G., Eiter, T.: Preferred Answer Sets for Extended Logic Programs. AI 109(1-2), 297–356 (1999)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Cali, A., Calvanese, D., De Giacomo, G., Lenzerini, M.: Data Integration under Integrity Constraints. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 262–279. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Cali, 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
  8. 8.
    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
  9. 9.
    Dung, P.M.: Integrating Data from Possibly Inconsistent Databases. CoopIS (1996)Google Scholar
  10. 10.
    Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)CrossRefGoogle Scholar
  11. 11.
    Greco, S., Saccà, D.: Search and Optimization Problems in Datalog. In: Computational Logic: Logic Programming and Beyond, pp. 61-82 (2002)Google Scholar
  12. 12.
    Greco, S., Zumpano, E.: Querying Inconsistent Database LPAR Conf., pp. 308-325 (2000)Google Scholar
  13. 13.
    Greco, G., Greco, S., Zumpano, E.: A Logic Programming Approach to the Integration, Repairing and Querying of Inconsistent Databases. In: ICLP Conf., pp. 348–364 (2001)Google Scholar
  14. 14.
    Greco, G., Greco, S., Zumpano, E.: A Logical Framework for Querying and Repairing Inconsistent Databases. IEEE Trans. Knowl. Data Eng 15(6), 1389–1408 (2003)CrossRefGoogle Scholar
  15. 15.
    Greco, S., Sirangelo, C., Trubitsyna, I., Zumpano, E.: Preferred Repairs for Inconsistent Databases. In: IDEAS Conf., pp. 202–211 (2003)Google Scholar
  16. 16.
    Grant, J., Subrahmanian, V.S.: Reasoning in Inconsistent Knowledge Bases. TKDE 7(1), 177–189 (1995)MathSciNetGoogle Scholar
  17. 17.
    Johnson, D.S.: A Catalog of Complexity Classes. In: van Leewen, J. (ed.) Handbook of Theoretical Computer Science, vol. 1, North-Holland, Amsterdam (1990)Google Scholar
  18. 18.
    Lin, J.: A Semantics for Reasoning Consistently in the Presence of Inconsistency. Artificial Intelligence 86(1), 75–95 (1996)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Marek, V.W., Truszczynski, M.: Revision Programming. Theoretical Computer Science 190(2), 241–277 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Papadimitriou, C.H.: Computational Complexity. Addison-Wesley, Reading (1994)zbMATHGoogle Scholar
  21. 21.
    Sakama, C., Tnoue, K.: Priorized logic programming and its application to commonsense reasoning. Artificial Intelligence (123), 185–222 (2000)Google Scholar
  22. 22.
    Selman, A.: A taxonomy of complexity classes of functions. JCSS 48, 327–381 (1994)MathSciNetGoogle Scholar
  23. 23.
    Ullman, J.K.: Principles of Database and Knowledge-Base Systems, vol. 1. Computer Science Press, Rockville (1988)Google Scholar
  24. 24.
    Wijsen, J.: Condensed Representation of Database Repairs for Consistent Query Answering. In: ICDT, pp. 378–393 (2003)Google Scholar
  25. 25.
    Zang, Y., Foo, N.: Answer sets for prioritized logic programs. In: ILPS, pp. 69–83 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sergio Greco
    • 1
  • Cristina Sirangelo
    • 1
  • Irina Trubitsyna
    • 1
  • Ester Zumpano
    • 1
  1. 1.DEISUniv della CalabriaRendeItaly

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