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Uncertainty pervades many domains in peoples' lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point is an important query type in these applications. In this paper, the authors study the problem of finding objects with the highest marginal probability of being the nearest neighbors to a query object. They adopt a general uncertainty model allowing for data and query uncertainty.
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