Feat-SKSJ: Fast and Exact Algorithm for Top-k Spatial-Keyword Similarity Join

Abstract

Due to the proliferation of GPS-enabled mobile devices and IoT environments, location-based services are generating a large number of objects that contain both spatial and keyword information, and spatial-keyword databases are receiving much attention. This paper addresses the problem of top-k spatial-keyword similarity join, which outputs k object pairs with the highest similarity. This query is a primitive operator for important applications, including duplicate detection, recommendation, and clustering. The main bottleneck of the top-k spatial-keyword similarity join is to compute the similarity of a given object pair. To avoid this computation as much as possible, a state-of-the-art algorithm utilizes a filter that can skip the exact similarity computation of a given pair. However, this algorithm suffers from a loose threshold at the first stage, a high filtering cost, and the impossibility of filtering many pairs in a batch. We propose Feat-SKSJ, which removes these drawbacks and quickly outputs the exact result. Extensive experiments on real datasets show that Feat-SKSJ is significantly faster than the state-of-the-art algorithm.