Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146856
Title: A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams
Authors: Wang, Jianhua
Ji, Bang
Lin, Feng
Lu, Shilei
Lan, Yubin
Cheng, Lianglun
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Wang, J., Ji, B., Lin, F., Lu, S., Lan, Y. & Cheng, L. (2020). A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams. International Journal of Distributed Sensor Networks, 16(10). https://dx.doi.org/10.1177/1550147720961336
Journal: International Journal of Distributed Sensor Networks
Abstract: Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.
URI: https://hdl.handle.net/10356/146856
ISSN: 1550-1329
DOI: 10.1177/1550147720961336
Rights: © 2020 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages(https://us.sagepub.com/en-us/nam/open-access-at-sage).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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