TY - GEN
T1 - A time-aware path-based publish/subscribe framework
AU - Jia, Mengdi
AU - Zhao, Yan
AU - Zheng, Bolong
AU - Liu, Guanfeng
AU - Zheng, Kai
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Nowadays, massive geo-tagged records are generated on the social media. These records are useful when the users intend to plan a trip and are interested in some specific topics along the trip. With such redundant records, a publish/subscribe system has been designed to allow the users who are interested in certain information (i.e., the subscribers) to receive messages from some message generators (i.e., the publishers). Existing efforts on publish/subscribe mainly focus on the textual content or the spatial location of the subscribers, while leaving the consideration of incorporating the subscribers’ moving behaviors and temporal information. Therefore, in this paper, we propose a Time-aware Path-based Publish/Subscribe (TPPS) model, where we propose a filtering-verification framework that contains two kinds of filters, i.e., time-aware location-based filter and time-aware region-based filter, with considering both temporal information and moving behaviors, and filtering unrelated subscriptions for each message. We evaluate the efficiency of our approach on a real-world dataset and the experimental results demonstrate the superiority of our method in both efficiency and effectiveness.
AB - Nowadays, massive geo-tagged records are generated on the social media. These records are useful when the users intend to plan a trip and are interested in some specific topics along the trip. With such redundant records, a publish/subscribe system has been designed to allow the users who are interested in certain information (i.e., the subscribers) to receive messages from some message generators (i.e., the publishers). Existing efforts on publish/subscribe mainly focus on the textual content or the spatial location of the subscribers, while leaving the consideration of incorporating the subscribers’ moving behaviors and temporal information. Therefore, in this paper, we propose a Time-aware Path-based Publish/Subscribe (TPPS) model, where we propose a filtering-verification framework that contains two kinds of filters, i.e., time-aware location-based filter and time-aware region-based filter, with considering both temporal information and moving behaviors, and filtering unrelated subscriptions for each message. We evaluate the efficiency of our approach on a real-world dataset and the experimental results demonstrate the superiority of our method in both efficiency and effectiveness.
KW - Path-based
KW - Publish/subscribe
KW - Time-aware
UR - http://www.scopus.com/inward/record.url?scp=85048031753&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91452-7_33
DO - 10.1007/978-3-319-91452-7_33
M3 - Article in proceeding
AN - SCOPUS:85048031753
SN - 9783319914510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 511
EP - 528
BT - Database Systems for Advanced Applications
PB - Springer
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Y2 - 21 May 2018 through 24 May 2018
ER -