New direction for uncertainty reasoning in deductive databases

This paper contributes a novel approach to nonmonotonic uncertainty reasoning, which is ubiquitous in many real-life applications. Founded on the paradigm of conditional probabilities we develop a rule-based calculus and prove that it is sound, even in the presence of incomplete information. Thus the merits of doing consistent judgments in uncertain domains and the advantages of modularity and incrementality of rulebased application development come together. We also can offer mechanisms to trace down inconsistencies that may be hidden in very large collections of uncertain rules. As next-generation applications will have to handle vast amounts of uncertain data, an integration into databases is mandatory. We give a direct implementation of our calculus on top of a database system with a DATALOG-interface. In this way we extend current database technology towards providing new applications with new suitable primitives and with a database platform for coping with uncertainty.