Abstract
In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users’ check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people’s movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users’ check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.