Tracing the lineage of view data in a warehousing environment
ACM Transactions on Database Systems (TODS)
Semantic integration of heterogeneous information sources
Data & Knowledge Engineering - Special issue on heterogeneous information resources need semantic access
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Lineage tracing for general data warehouse transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Synthesizing an Integrated Ontology
IEEE Internet Computing
Completeness of integrated information sources
Information Systems - Special issue: Data quality in cooperative information systems
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ACM Computing Surveys (CSUR)
Perm: Processing Provenance and Data on the Same Data Model through Query Rewriting
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
Data lineage in the MOMIS data fusion system
ICDEW '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops
Consistent query answering: five easy pieces
ICDT'07 Proceedings of the 11th international conference on Database Theory
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A fundamental task in data integration is data fusion, the process of fusing multiple records representing the same real-world object into a consistent representation; data fusion involves the resolution of possible conflicts between data coming from different sources; several high level strategies to handle inconsistent data have been described and classified in [8]. The MOMIS Data Integration System [2] uses either conflict avoiding strategies (such as the trust your friends strategy which takes the value of a preferred source) and resolution strategies (such as the meet in the middle strategy which takes an average value). In this paper we consider other strategies proposed in literature to handle inconsistent data and we discuss how they can be adopted and extended in the MOMIS Data Integration System. First of all, we consider the methods introduced by the Trio system [1,6] and based on the idea to tackle data conflicts by explicitly including information on provenance to represent uncertainty and use it to answer queries. Other possible strategies are to ignore conflicting values at the global level (i.e., only consistent values are considered) and to consider at the global level all conflicting values. The original contribution of this paper is a provenance-based framework which includes all the above mentioned conflict handling strategies and use them as different search strategies for querying the integrated sources.