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A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems
 ACM Transactions on Information Systems
, 1994
"... We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression ..."
Abstract

Cited by 172 (30 self)
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We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression always confirm to the underlying probabilistic model. We also show for which expressions extensional semantics yields the same results. Furthermore, we discuss complexity issues and indicate possibilities for optimization. With regard to databases, the approach allows for representing imprecise attribute values, whereas for information retrieval, probabilistic document indexing and probabilistic search term weighting can be modelled. As an important extension, we introduce the concept of vague predicates which yields a probabilistic weight instead of a Boolean value, thus allowing for queries with vague selection conditions. So PRA implements uncertainty and vagueness in combination with the...
Supporting ValidTime Indeterminacy
 ACM Transactions on Database Systems
, 1998
"... In validtime indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support validtime indeterminacy. We represent the occurrence time of an event with a set of possible insta ..."
Abstract

Cited by 86 (17 self)
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In validtime indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support validtime indeterminacy. We represent the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set. We also describe query language constructs to retrieve information in the presence of indeterminacy. These constructs enable users to specify their credibility in the underlying data and their plausibility in the relationships among that data. A denotational semantics for SQL’s select statement with optional credibility and plausibility constructs is given. We show that this semantics is reliable, in that it never produces incorrect information, is maximal, in that if it were extended to be more informative, the results may not be reliable, and reduces to the previous semantics when there is no indeterminacy. Although the extended data model and query language provide needed modeling capabilities, these extensions appear initially to carry a significant execution cost. A contribution of this paper is to demonstrate that our approach is useful and practical. An efficient representation of validtime indeterminacy and efficient query processing algorithms are provided. The cost of
Historical Indeterminacy
, 1992
"... In historical indeterminacy, it is known that an event stored in a temporal database did in fact occur, but it is not known exactly when the event occurred. We present the possible tuples data model, in which each indeterminate event is represented with a set of possible events that delimits when ..."
Abstract

Cited by 7 (3 self)
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In historical indeterminacy, it is known that an event stored in a temporal database did in fact occur, but it is not known exactly when the event occurred. We present the possible tuples data model, in which each indeterminate event is represented with a set of possible events that delimits when the event might have occurred, and a probability distribution over that set. We extend the TQuel query language with constructs that specify the user's credibility in the underlying historical data and in the user's plausibility in the relationships among that data. We provide a formal tuple calculus semantics, and show that this semantics reduces to the determinate semantics. We outline an efficient representation of historical indeterminacy, and efficient query processing algorithms, demonstrating the practicality of our proposed approach. 1 Department of Computer Science University of Arizona Tucson, AZ 85721 fcurtis,rtsg@cs.arizona.edu Historical Indeterminacy 1 Copyright c...