TY - GEN
T1 - LTCEP
T2 - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
AU - Ma, Meng
AU - Wang, Ping
AU - Chu, Chao Hsien
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Complex event processing has been widely adopted in different domains, from large-scale sensor networks, smart home, trans-portation, to industrial monitoring, providing the ability of intelligent procession and decision making supporting. In many application scenarios, a lot of complex events are long-Term, which takes a long time to happen. Processing long-Term complex event with traditional approaches usually leads to the increase of runtime states and therefore impact the processing performance. Hence, it requires an efficient long-Term event processing approach and intermediate results storage/query policy to solve this type of problems. In this paper, we propose an event processing system, LTCEP, for long-Term event. In LTCEP, we leverage the semantic constraints calculus to split a long-Term event into two parts, online detection and event buffering respectively. A long-Term query mechanism and event buffering structure are established to optimize the fast response ability and processing performance. Experiments prove that, for long-Term event processing, LTCEP model can effectively reduce the redundant runtime state, which provides a higher response performance and system throughput comparing to other selected benchmarks.
AB - Complex event processing has been widely adopted in different domains, from large-scale sensor networks, smart home, trans-portation, to industrial monitoring, providing the ability of intelligent procession and decision making supporting. In many application scenarios, a lot of complex events are long-Term, which takes a long time to happen. Processing long-Term complex event with traditional approaches usually leads to the increase of runtime states and therefore impact the processing performance. Hence, it requires an efficient long-Term event processing approach and intermediate results storage/query policy to solve this type of problems. In this paper, we propose an event processing system, LTCEP, for long-Term event. In LTCEP, we leverage the semantic constraints calculus to split a long-Term event into two parts, online detection and event buffering respectively. A long-Term query mechanism and event buffering structure are established to optimize the fast response ability and processing performance. Experiments prove that, for long-Term event processing, LTCEP model can effectively reduce the redundant runtime state, which provides a higher response performance and system throughput comparing to other selected benchmarks.
UR - http://www.scopus.com/inward/record.url?scp=84964562376&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964562376&partnerID=8YFLogxK
U2 - 10.1109/DSDIS.2015.54
DO - 10.1109/DSDIS.2015.54
M3 - Conference contribution
AN - SCOPUS:84964562376
T3 - Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
SP - 548
EP - 555
BT - Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
A2 - Yang, Laurence T.
A2 - Chen, Jinjun
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2015 through 13 December 2015
ER -