Incomplete Information in Relational Databases
Journal of the ACM (JACM)
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Information Sciences: an International Journal
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Journal of the ACM (JACM)
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Generic Schema Matching with Cupid
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VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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ADBIS '98 Proceedings of the Second East European Symposium on Advances in Databases and Information Systems
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BNCOD 15 Proceedings of the 15th British National Conferenc on Databases: Advances in Databases
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The VLDB Journal — The International Journal on Very Large Data Bases
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Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
HePToX: marrying XML and heterogeneity in your P2P databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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Knowledge and Information Systems
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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
Structured reachability analysis for Markov decision processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Sum-max monotonic ranked joins for evaluating top-k twig queries on weighted data graphs
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
The VLDB Journal — The International Journal on Very Large Data Bases
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Data conflict resolution using trust mappings
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WAIM'10 Proceedings of the 11th international conference on Web-age information management
R2DF framework for ranked path queries over weighted RDF graphs
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Reducing uncertainty of schema matching via crowdsourcing
Proceedings of the VLDB Endowment
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A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolutioncan be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR1) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data informsover ↔ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.