Efficient Processing of Top-k Spatial Preference Queries

Top-k spatial preference queries arrival a ranked set of the k best data objects based on the scores of mark objects in their spatial neighborhood. Despite the wide assortment of location-based applications that rely on spatial predilection queries, existing algorithms incur non-negligible processing cost resulting in high retort time. The reason is that computing the score of an information object requires examining its spatial locality to find the feature object with the highest score. In this paper, we suggest a novel technique to speed up the performance of top-k spatial predilection queries. To this end, we propose a mapping of pairs of information and feature objects to a distance-score space, which in rotate allows us to identify and materialize the minimal subset of pairs that is adequate to answer any spatial preference query. Furthermore, we present a novel algorithm that improves uncertainty processing performance by avoiding examining the spatial neighborhood of the information objects during query execution. In addition, we recommend an efficient algorithm for materialization and we express useful properties that reduce the cost of maintenance. We show through wide experiments that our approach significantly reduces the number of I/Os and completing time compared to the state-of-the-art algorithms for dissimilar setups.