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. |
Year | DOI | Venue |
---|---|---|
2015 | 10.1109/DSDIS.2015.54 | DSDIS |
Keywords | Field | DocType |
Long-Term, Complex Event Processing, Internet of Things, Semantic, Data Stream, Ontology | Data stream mining,Computer science,Data stream,Complex event processing,Real-time computing,Home automation,Event (computing),Throughput,Wireless sensor network,Semantics | Conference |
Citations | PageRank | References |
4 | 0.38 | 8 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Meng Ma | 1 | 78 | 15.71 |
Ping Wang | 2 | 75 | 9.22 |
Chao-Hsien Chu | 3 | 711 | 48.98 |