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2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS) (2015)
Sydney, Australia
Dec. 11, 2015 to Dec. 13, 2015
ISBN: 978-1-5090-0214-6
pp: 548-555
ABSTRACT
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.
INDEX TERMS
Semantics, Ontologies, Calculus, Event detection, Rail transportation, Monitoring, Control systems
CITATION

M. Ma, P. Wang and C. Chu, "LTCEP: Efficient Long-Term Event Processing for Internet of Things Data Streams," 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), Sydney, Australia, 2015, pp. 548-555.
doi:10.1109/DSDIS.2015.54
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