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A Novel Complex Event Processing Engine for Intelligent Data Analysis in Integrated Information Systems
Author(s) -
Dong Wang,
Mingquan Zhou,
Sajid Ali,
Pengbo Zhou,
Yusong Liu,
Xuesong Wang
Publication year - 2016
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2016/6741401
Subject(s) - computer science , complex event processing , lida , event (particle physics) , data mining , big data , distributed computing , semantic reasoner , filter (signal processing) , artificial intelligence , cognitive architecture , operating system , physics , cognition , process (computing) , quantum mechanics , neuroscience , computer vision , biology
Novel and effective engines for data analysis in integrated information systems are urgently required by diverse applications, in which massive business data can be analyzed to enable the capturing of various situations in real time. The performance of existing engines has limited capacity of data processing in distributed computing. Although Complex Event Processing CEP has enhanced the capacity of data analysis in information systems, it is still a big challenging task since events are rapidly increasing in diverse applications. In this paper, a lightweight intelligent data analysis system with a novel CEP engine named LIDA-E is introduced, which employs the knowledge base with rules and an event processing algorithm for analysis. Event models as well as operators support the rules for event selection and aggregation. These operators and rules have been utilized for constructing new CEP system architecture which combines expressiveness and efficiency in analysis. It adopts the agents and filter conception explicitly to provide the event transmission mechanism efficiently. Finally, the comparison between the proposed engine and the existing engine shows that LIDA-E has 48.65% averagely reduced time cost in different tests. The experimental results demonstrate that the developed architecture has better performance in both transmitting and analyzing a large number of events.

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