Probabilistic Nearest-Neighbor Query on Uncertain Objects

摘要: Nearest-neighbor queries are an important query type for commonly used feature databases. In many different application areas, e.g. sensor databases, location based services or face recognition systems, distances between objects have to be computed on vague and uncertain data. A successful approach is express the distance two by probability density functions which assign a value each possible value. By integrating complete probabilistic function as whole directly into algorithm, full information provided these exploited. The result of such algorithm consists tuples containing object indicating likelihood that satisfies t he predicate. this paper we introduce efficient strategy cessing nearest-neighbor queries, computation values very expensive. detailed experimental evaluation, demonstrate benefits our approach. experiments show can achieve high quality results with rather low computational cost.