Tackling inconsistencies in data integration through source preferences

Dealing with inconsistencies is one the main challenges in data integration systems, where data stored in the local sources may violate integrity constraints specified at the global level. Recently, declarative approaches have been proposed to deal with such a problem. Existing declarative proposals do not take into account preference assertions specified between sources when trying to solve inconsistency. On the other hand, the designer of an integration system may often include in the specification preference rules indicating the quality of data sources. In this paper, we consider Local-As-View integration systems, and propose a method that allows one to assign formal semantics to a data integration system whose declarative specification includes information on source preferences. To the best of our knowledge, our approach is the first one to consider in a declarative way information on source quality for dealing with inconsistent data in Local-As-View integration systems.

[1]  Yannis Papakonstantinou,et al.  Object Fusion in Mediator Systems , 1996, VLDB.

[2]  Thomas Eiter,et al.  Efficient Evaluation of Logic Programs for Querying Data Integration Systems , 2003, ICLP.

[3]  Jan Chomicki,et al.  Consistent Answers from Integrated Data Sources , 2002, FQAS.

[4]  Michael R. Genesereth,et al.  Infomaster: an information integration system , 1997, SIGMOD '97.

[5]  Silvana Castano,et al.  Information Integration: The MOMIS Project Demonstration , 2000, VLDB.

[6]  M. Tamer Özsu,et al.  Conflict tolerant queries in AURORA , 1999, Proceedings Fourth IFCIS International Conference on Cooperative Information Systems. CoopIS 99 (Cat. No.PR00384).

[7]  Gerhard Lakemeyer,et al.  The logic of knowledge bases , 2000 .

[8]  Maurizio Lenzerini,et al.  Source inconsistency and incompleteness in data integration , 2002, KRDB.

[9]  Alon Y. Halevy,et al.  Answering queries using views: A survey , 2001, The VLDB Journal.

[10]  Jan Chomicki,et al.  Consistent query answers in inconsistent databases , 1999, PODS '99.

[11]  Sergio Greco,et al.  A Logical Framework for Querying and Repairing Inconsistent Databases , 2003, IEEE Trans. Knowl. Data Eng..

[12]  Maurizio Lenzerini,et al.  Introduction to the special issue on data extraction, cleaning, and reconciliation , 2001, Inf. Syst..

[13]  Jeffrey D. Ullman,et al.  Information integration using logical views , 1997, Theor. Comput. Sci..

[14]  Alon Y. Levy Logic-based techniques in data integration , 2001 .

[15]  Felix Naumann,et al.  Quality-driven Integration of Heterogenous Information Systems , 1999, VLDB.

[16]  Maurizio Lenzerini,et al.  Editorial: Introduction to: Data extraction, cleaning, and reconciliation a special issue of information systems, an international journal , 2001 .

[17]  Divesh Srivastava,et al.  The Information Manifold , 1995 .

[18]  Jan Chomicki,et al.  Specifying and Querying Database Repairs using Logic Programs with Exceptions , 2000, FQAS.

[19]  Ioana Manolescu,et al.  Answering XML Queries on Heterogeneous Data Sources , 2001, VLDB.

[20]  Andrea Calì,et al.  Query rewriting and answering under constraints in data integration systems , 2003, IJCAI.

[21]  Andrea Calì,et al.  On the decidability and complexity of query answering over inconsistent and incomplete databases , 2003, PODS.

[22]  Leopoldo E. Bertossi,et al.  Logic Programs for Consistently Querying Data Integration Systems , 2003, IJCAI.

[23]  Alberto O. Mendelzon,et al.  Merging Databases Under Constraints , 1998, Int. J. Cooperative Inf. Syst..

[24]  Diego Calvanese,et al.  Answering Queries Using Views over Description Logics Knowledge Bases , 2000, AAAI/IAAI.

[25]  François Bry,et al.  Query Answering in Information Systems with Integrity Constraints , 1997, IICIS.

[26]  Maurizio Lenzerini,et al.  Data integration: a theoretical perspective , 2002, PODS.

[27]  Amihai Motro,et al.  Fusionplex: resolution of data inconsistencies in the integration of heterogeneous information sources , 2006, Inf. Fusion.