A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 17th International Conference on Data Engineering
Managing uncertainty in sensor database
ACM SIGMOD Record
Fuzzy Databases: Modeling, Design, and Implementation
Fuzzy Databases: Modeling, Design, and Implementation
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Orion 2.0: native support for uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Similarity search and mining in uncertain databases
Proceedings of the VLDB Endowment
Quantum Computation and Quantum Information: 10th Anniversary Edition
Quantum Computation and Quantum Information: 10th Anniversary Edition
A top-k filter for logic-based similarity conditions on probabilistic databases
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
ProQua: a system for evaluating logic-based scoring functions on uncertain relational data
Proceedings of the 16th International Conference on Extending Database Technology
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A Boolean logic-based evaluation of a database query returns true on match and false on mismatch. Unfortunately, there are many application scenarios where such an evaluation is not possible or does not adequately meet user expectations about vague and uncertain conditions. Consequently, there is a need for incorporating impreciseness and proximity into a logic-based query language. A probabilistic approach known from Information Retrieval expresses the fulfilling of a condition by a probability of relevance. Besides relevance probabilities used in IR probabilistic databases have been established as a challenging research field. In this work we lay the theoretical basis for the combination of relevance probabilities and probabilistic databases evaluated by a unifying probability measure.