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<p><b>Abstract</b>—In recent years, several approaches have been proposed for representing uncertain data in a database. These approaches have typically extended the relational model by incorporating probability measures to capture the uncertainty associated with data items. However, previous research has not directly addressed the issue of normalization for reducing data redundancy and data anomalies in probabilistic databases. In this paper, we examine this issue. To that end, we generalize the concept of functional dependency to stochastic dependency and use that to extend the scope of normal forms to probabilistic databases. Our approach is a consistent extension of the conventional normalization theory and reduces to the latter.</p>
Probabilistic relational model, data uncertainty, belief network, functional dependency, stochastic dependency, probabilistic normal form

D. Dey and S. Sarkar, "Generalized Normal Forms for Probabilistic Relational Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 485-497, 2002.
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