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Top-k spatial preference queries return a ranked set of the k best data objects based on the scores of feature objects and shortest path distance in their spatial neighborhood. Despite the wide range of location-based applications that rely on spatial preference queries, existing algorithms incur non-negligible processing cost resulting in high response time. The reason is that computing the score of a data object requires examining its spatial neighborhood to find the feature object with highest score. Here a mapping of pairs of data and feature objects to a distance-score space, which in turn allows users to identify and materialize the minimal subset of pairs that is sufficient to answer any spatial preference query.
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