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A Probabilistic NF2 Relational Algebra for Imprecision in
, 1996
"... We present a probabilistic data model which is based on relations in nonfirstnormalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. This way, imprecise attribute values are modelled as a probabilistic subrelation. ..."
Abstract

Cited by 13 (2 self)
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We present a probabilistic data model which is based on relations in nonfirstnormalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. This way, imprecise attribute values are modelled as a probabilistic subrelation.
A Probabilistic NF2 Relational Algebra for Integrated Information Retrieval and Database Systems
 In Proceedings of the 2nd World Conference on Integrated Design and Process Technology
, 1996
"... The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in nonfirst ..."
Abstract

Cited by 10 (1 self)
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The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in nonfirst normalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Thus, the set of weighted index terms of a document are represented as a probabilistic subrelation. In a similar way, imprecise attribute values are modelled as a setvalued attribute. We redefine the relational operators for this type of relations such that the result of each operator is again a probabilistic NF2 relation, where the weight of a tuple gives the probability that this tuple belongs to the result. By ordering the tuples according to decreasing probabilities, the model yields a ranking of answers like in most IR models. This effect also can be used for ...