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There are a lot of application domains, e.g. sensor databases, traffic management or recognition systems, where objects have to be compared based on vague and uncertain data. Feature databases with uncertain data require special methods for effective similarity search. In this paper, the authors propose an effective and efficient probabilistic similarity ranking algorithm that exploits the full information given by inexact object representations. Thereby, they assume that the objects are given in form of discrete probabilistic object locations in particular several object snapshots with confidence values.
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