Efficient Processing of Top-k Spatial Preference Queries

Top-k spatial preference queries return a ranked set of the k best data objects based on the scores of feature objects 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. In this paper, we propose a novel technique to speed up the performance of top-k spatial preference queries. To this end, we propose a mapping of pairs of data and feature objects to a distance-score space, which in turn allows us to identify and materialize the minimal subset of pairs that is sufficient to answer any spatial preference query. Furthermore, we present a novel algorithm that improves query processing performance by avoiding examining the spatial neighborhood of the data objects during query execut...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: João B. Rocha-Junior, Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg
• Keywords: Feature Objects, PVLDB 2010, Spatial Neighborhood, Spatial Preference