A Relational Database Model with Interval Probability Valued Attributes for Uncertain and Imprecise Information
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Abstract
Although the conventional relational database model (CRDB) is benecial to model, design, and implement large-scale systems, it is limited to express and deal with uncertain and imprecise information. In this paper, we introduce a new relational database model as an extension of CRDB where relational attributes may take a value associated with a probability interval, named IPRDB, for representing and handling uncertain and imprecise information in practice. To build IPRDB, we employ three key methods: (1) Probabilistic values of data types are proposed for expressing uncertain and imprecise valued attributes; (2) the probabilistic interpretations of binary relations on sets and operators on probability intervals are used for computing the uncertain degree of functional dependencies, keys, and relations on value domains of attributes; and (3) the combination strategies of probabilistic values are dened for developing new relational algebraic operations. Then, fundamental concepts of the model, such as schemas, probabilistic relations, and probabilistic relational databases, are extended coherently and consistently with those of the conventional relational database model. A set of the properties of the basic probabilistic relational algebraic operations is also formulated and proven. The built IPRDB model can represent and manipulate effectively uncertain and imprecise information in real-world applications.
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