Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (k ALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of k ALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in k ALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process k ALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to schedule multiple query regions. Finally, we conduct extensive experiments on real and synthetic spatial data to demonstrate the performance of the developed solutions.
|Original language||English (US)|
|Number of pages||16|
|Journal||World Wide Web|
|State||Published - Mar 17 2018|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This paper is partly supported by Natural Science Foundation of P.R.China (No. 61373147, and No. 61672442), Fujian Province Science and Technology Plan Project (No. 2016Y0079, No. 2017J01783), the Education and Scientific Research Project for Youth and Middle-aged Teachers in Fujian (No. JA15365), the Open Research Fund Program of Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, and the Open Research Fund Program of Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University.