Answering heterogeneous database queries with degrees of uncertainty
Distributed and Parallel Databases
IEEE Transactions on Knowledge and Data Engineering
An Evidential Reasoning Approach to Attribute Value Conflict Resolution in Database Integration
IEEE Transactions on Knowledge and Data Engineering
Processing Queries Over Generalization Hierarchies in a Multidatabase System
VLDB '83 Proceedings of the 9th International Conference on Very Large Data Bases
Clean Answers over Dirty Databases: A Probabilistic Approach
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
ACM Computing Surveys (CSUR)
Probabilistic Event Extraction from RFID Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Qualitative effects of knowledge rules and user feedback in probabilistic data integration
The VLDB Journal — The International Journal on Very Large Data Bases
Generic entity resolution with negative rules
The VLDB Journal — The International Journal on Very Large Data Bases
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Today, probabilistic databases (PDB) become helpful in several application areas. In the context of cleaning a single PDB or integrating multiple PDBs, duplicate tuples need to be merged. A basic approach for merging probabilistic tuples is simply to build the union of their sets of possible instances. In a merging process, however, often additional domain knowledge or user expertise is available. For that reason, in this paper we extend the basic approach with aggregation functions, knowledge rules, and instance weights for incorporating external knowledge in the merging process.