Information stored in a database is often subject to uncertainty and imprecision. Probability theory provides a well-known and well understood way of representing uncertainty and may thus be used to provide a mechanism for storing uncertain information in a database. We consider the problem of aggregation using an imprecise probability data model that allows us to represent imprecision by partial probabilities and uncertainty using probability distributions. Most work to date has concentrated on providing functionality for extending the relational algebra with a view to executing traditional queries on uncertain or imprecise data. However, for imprecise and uncertain data, we often require aggregation operators that provide information on patterns in the data. Thus, while traditional query processing is tuple-driven, processing of uncertain data is often attribute-driven where we use aggregation operators to discover attribute properties. The aggregation operator that we define uses the Kullback-Leibler information divergence between the aggregated probability distribution and the individual tuple values to provide a probability distribution for the domain values of an attribute or group of attributes. The provision of such aggregation operators is a central requirement in furnishing a database with the capability to perform the operations necessary for knowledge discovery in databases.
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 1 Nov 2001|