Uncertain Relational Database Management and Integration Systems: Concepts and Techniques

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Many applications in diverse domains require the ability to store and process voluminous uncertain data. While traditional database management systems (DBMS) have been quite successful in handling precise data and missing values, they offer little or no support for uncertain data. What is also needed is the ability to "quantify" the integrity of the data. Informally, a piece of data is uncertain if its truth is not established definitely. Uncertainty has been studied extensively for over two decades in database and AI communities, resulting in a substantial body of principles, concepts, representations, and algorithms. However, more work is required to develop better solutions for a full realization of uncertain data management. This explains recent increased attention to the subject in database research, and the motivation of this proposal. The major problems include (1) semantics (representations), (2) evaluation (correct and efficient processing),