Probabilistic reasoning in intelligent systems: networks of plausible inference
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Interest in XML databases has been growing over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML (PIXml for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXml data. The first semantics is a "global" semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a "local" semantics which is more amenable to computation. We prove several results linking the two semantics together. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to compute answers to queries and that is correct for a large class of probabilistic instances.