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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS, pp. 68–79 (1999)
Arenas, M., Bertossi, L.E., Chomicki, J.: Answer sets for consistent query answering in inconsistent databases. TPLP 3(4-5), 393–424 (2003)
Baral, C., Kraus, S., Minker, J.: Combining multiple knowledge bases. TKDE 3(2), 208–220 (1991)
Belnap, N.: A useful four valued logic. Modern Uses of Many Valued Logic, 8–37 (1977)
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)
Besnard, P.: Remedying inconsistent sets of premises. Int. J. Approx. Reasoning 45(2), 308–320 (2007)
Blair, H.A., Subrahmanian, V.S.: Paraconsistent logic programming. Theor. Comp. Sci. 68(2), 135–154 (1989)
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)
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)
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)
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)
Chomicki, J., Marcinkowski, J.: Minimal-change integrity maintenance using tuple deletions. Inf. Comp. 197(1-2), 90–121 (2005)
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)
da Costa, N.: On the theory of inconsistent formal systems. N. Dame J. of Formal Logic 15(4), 497–510 (1974)
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)
Fitting, M.: Bilattices and the semantics of logic programming. J. of Log. Prog. 11(1-2), 91–116 (1991)
Flesca, S., Furfaro, F., Parisi, F.: Querying and repairing inconsistent numerical databases. ACM Trans. Database Syst. 35 (2) (2010)
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)
Fuxman, A., Miller, R.J.: First-order query rewriting for inconsistent databases. J. Comput. Syst. Sci. 73(4), 610–635 (2007)
Grant, J., Hunter, A.: Measuring inconsistency in knowledgebases. J. of Intel. Inf. Syst. 27(2), 159–184 (2006)
Greco, G., Greco, S., Zumpano, E.: A logical framework for querying and repairing inconsistent databases. IEEE TKDE 15(6), 1389–1408 (2003)
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)
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)
Kifer, M., Lozinskii, E.L.: A logic for reasoning with inconsistency. J. of Autom. Reas. 9(2), 179–215 (1992)
Lozinskii, E.L.: Resolving contradictions: A plausible semantics for inconsistent systems. J. of Autom. Reas. 12(1), 1–31 (1994)
Martinez, M.V., Parisi, F., Pugliese, A., Simari, G.I., Subrahmanian, V.S.: Inconsistency management policies. In: KR, pp. 367–377 (2008)
Reiter, R.: A logic for default reasoning. Artif. Intel. 13(1-2), 81–132 (1980)
Staworko, S., Chomicki, J.: Consistent query answers in the presence of universal constraints. Inf. Syst. 35(1), 1–22 (2010)
Subrahmanian, V.S., Amgoud, L.: A general framework for reasoning about inconsistency. In: IJCAI, pp. 599–504 (2007)
Touretzky, D.: The mathematics of inheritance systems. Morgan Kaufmann, San Francisco (1986)
Wijsen, J.: Database repairing using updates. ACM TODS 30(3), 722–768 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martinez, M.V., Parisi, F., Pugliese, A., Simari, G.I., Subrahmanian, V.S. (2010). Efficient Policy-Based Inconsistency Management in Relational Knowledge Bases. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-15951-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15950-3
Online ISBN: 978-3-642-15951-0
eBook Packages: Computer ScienceComputer Science (R0)