Please use this identifier to cite or link to this item:
|Title:||A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams||Authors:||Wang, Jianhua
|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|
Updated on May 25, 2022
Updated on May 25, 2022
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.