Parameterized spatial query processing based on social probabilistic clustering

Liang Tang, Haiquan Chen, Wei-Shinn Ku, Min-Te Sun

Abstract: In this paper, we propose two parameterized frameworks, namely the Uniform Watchtower (UW) framework and the Hot zone-based Watchtower (HW) framework, for the evaluation of spatial queries on large road networks. The motivation of this research is twofold: (1) how to answer spatial queries efficiently on large road networks with massive POI data and (2) how to take advantage of social data in spatial query processing. In UW, the network traversal terminates once it acquires the Point of Interest (POI) distance information stored in watchtowers. In HW, by observing that users' movements often exhibit strong spatial patterns, we employ probabilistic clustering to model mobile user check-in data as a mixture of 2-dimensional Gaussian distributions to identify hot zones so that watchtowers can be deployed discriminatorily. Our analyses verify the superiority of HW over UW in terms of query response time.