Assumption Queries Processing of Probabilistic Relational Databases

Abstract

Many prevail applications, such as data cleaning, sensor networks, tracking moving objects, emerge an increasing demand for managing uncertain data. Probabilistic relational databases support uncertain data management. Informally, a probabilistic database is a probability distribution over a set of deterministic databases (namely, possible worlds). Assumption queries in probabilistic relational databases have natural and important applications. To avoid unnecessary updates of probabilistic relational databases in existing general methods of assumption queries processing, an optimization method by computing conditional probability is proposed to handle assumption queries. The effectiveness of the optimization strategies for assumption queries is demonstrated in the experiment.