Saturn: A Fast Keyword kNN Search System in Road Networks

Abstract

Location-based services (LBS) have became more and more popular nowadays since people equipped with smart phones. Existing keyword k-nearest neighbor (kNN) search methods focus more on the keywords; therefore, they use direct distance of two points, also known as, Euclidean distance as spatial constraints. However, the nearest point-of-interest (POI) returned by these services may not be the nearest on the road networks. For some services that consider the road networks, they use road expansion methods to solve this problem. The speed limitation for a large road network and index structures for millions POI may be the bottlenecks for these services. To address those problems, we develop a fast keyword kNN search system in road networks, called Saturn. Instead of using road expansion methods, we introduce a grid-based shortest path computation method, a filter-and-verification framework to search fast in road networks, and we also devise an improvement of the grid index to further improve the performance. We conduct extensive experiments on real data sets, and the experimental results show that our method is efficient and scalable to large data sets, significantly outperforming state-of-the-art methods.