Efficient collective spatial keyword query processing on road networks

• Main Authors: GAO, Yunjun, ZHAO, Jingwen, ZHENG, Baihua, CHEN, Gang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Algorithm; collective; road network; spatial keyword query; Databases and Information Systems; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/3149; https://ink.library.smu.edu.sg/context/sis_research/article/4149/viewcontent/Efficient_collective_spatial_keyword_query_processing_on_road_networks.pdf
• Institution: Singapore Management University

The collective spatial keyword query (CSKQ), an important variant of spatial keyword queries, aims to find a set of the objects that collectively cover users' queried keywords, and those objects are close to the query location and have small inter-object distances. Existing works only focus on the CSKQ problem in the Euclidean space, although we observe that, in many real-life applications, the closeness of two spatial objects is measured by their road network distance. Thus, existing methods cannot solve the problem of network-based CSKQ efficiently. In this paper, we study the problem of collective spatial keyword query processing on road networks, where the objects are located on a predefined road network. We first prove that this problem is NP-complete, and then we propose two approximate algorithms with provable approximation bounds and one exact algorithm, for supporting CSKQ on road networks efficiently. Extensive experiments using real datasets demonstrate the efficiency and accuracy of our presented algorithms.