Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Implementing imprecision in information systems
Information Sciences: an International Journal - Special issue on expert systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A probabilistic relational model and algebra
ACM Transactions on Database Systems (TODS)
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
An Algebra for Probabilistic Databases
IEEE Transactions on Knowledge and Data Engineering
The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Computing a k-route over uncertain geographical data
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Association rule mining: models and algorithms
Association rule mining: models and algorithms
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Queries on probabilistic databases would be based on approximate matching rather than exact matching. This is partly due to the fact that the user may not know what are the exact probabilities of objects in a database. On the other hand, the domain of the attribute of a 1NF relational scheme is generally required finite. But the domain (0, 1] of the attribute that describes the probabilistic significance of an object is infinite. This means that it does not seem appropriate for approximate queries. In order to perform anything useful, a probabilistic data model is advocated for representing probabilistic data in this paper. The model is based on our definition of the nearest neighbor of data, which is used to measure the equality of probabilistic data. As a result, the approximation and infinite semantics of probabilistic data can be modeled in the nearest neighbor. Furthermore, a probabilistic relational algebra is also proposed so as to approximately query such databases.