TY - GEN
T1 - Continuous Geo-Social Group Monitoring over Moving Users
AU - Zhu, Huaijie
AU - Liu, Wei
AU - Yin, Jian
AU - Wang, Mengxiang
AU - Xu, Jianliang
AU - Huang, Xin
AU - Lee, Wang Chien
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85136438921&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136438921&partnerID=8YFLogxK
U2 - 10.1109/ICDE53745.2022.00028
DO - 10.1109/ICDE53745.2022.00028
M3 - Conference contribution
AN - SCOPUS:85136438921
T3 - Proceedings - International Conference on Data Engineering
SP - 312
EP - 324
BT - Proceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PB - IEEE Computer Society
T2 - 38th IEEE International Conference on Data Engineering, ICDE 2022
Y2 - 9 May 2022 through 12 May 2022
ER -