Continuous Geo-Social Group Monitoring over Moving Users

Recently a lot of research works have focused on geo-social group queries for group-based activity planning and scheduling in location-based social networks (LBSNs), which return a social cohesive user group with a spatial constraint. However, existing studies on geo-social group queries assume the users are stationary whereas in real LBSN applications all users may continuously move over time. Thus, in this paper we in-vestigate the problem of continuous geo-social groups monitoring (CGSGM) over moving users. A challenge in answering CGSGM queries over moving users is how to efficiently update geo-social groups when users are continuously moving. To address the CGSGM problem, we first propose a baseline algorithm, namely Baseline-BB, which recomputes the new geo-social groups from scratch at each time instance by utilizing a branch and bound (BB) strategy. To improve the inefficiency of BB, we propose a new strategy, called common neighbor or neighbor expanding (CNNE), which expands the common neighbors of edges or the neighbors of users in intermediate groups to quickly produce the valid group combinations. Based on CNNE, we propose another baseline algorithm, namely Baseline-CNNE. As these baseline algorithms do not maintain any intermediate results to facilitate further query processing, we develop an incremental algorithm, called incremental monitoring algorithm (IMA), which maintains the support, common neighbors and the neighbors of current users when exploring possible user groups for further updates and query processing. Finally, we conduct extensive experiments using three real datasets to validate our ideas and evaluate the proposed algorithms,