Efficient time-interval augmented spatial keyword queries on road networks

Abstract

In this paper, we study the efficient Time-interval Augmented Spatial Keyword (TASK) query problem, which considers the location, time-interval, and attribute value of keywords of spatial objects on road networks. We propose the concept of keyword hot value which is usually the value of textual attribute, such as popularity and price, and design a novel similarity function to evaluate the similarity between spatial textual objects and the query. A hierarchical index GI-tree is proposed to handle the TASK query with a best-first strategy. To improve the query efficiency, a novel hybrid index, termed SGI, is designed for pruning unqualified objects by utilizing their spatial, textual and temporal information simultaneously. Furthermore, a tight upper bound score, as well as the heuristic searches is presented to get further optimization, and a search framework is designed to obtain the top-k results in an efficient way. Finally, extensive experiments on real-world datasets demonstrate the efficiency and scalability of our proposed solution.