Efficient Top-k Spatial Distance Joins

Abstract

Consider two sets of spatial objects R and S, where each object is assigned a score (e.g., ranking). Given a spatial distance threshold ε and an integer k, the top-k spatial distance join (k- SDJ) returns the k pairs of objects, which have the highest combined score (based on an aggregate function γ) among all object pairs in R×S which have spatial distance at most ε. Despite the practical application value of this query, it has not received adequate attention in the past. In this paper, we fill this gap by proposing methods that utilize both location and score information from the objects, enabling top-k join computation by accessing a limited number of objects. Extensive experiments demonstrate that a technique which accesses blocks of data from R and S ordered by the object scores and then joins them using an aR-tree based module performs best in practice and outperforms alternative solutions by a wide margin.