Title: An alarm data mining method for operation and maintenance support network based on stream computing

Authors: Liyu Huang; Chang Liu; Yin Zheng; Hailin Zhao

Addresses: Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China ' Guangzhou Ji Neng Information Technology Co., Ltd., Guangzhou, Guangdong, China

Abstract: Communication system failure will produce a large number of alarm data. If it cannot be mined on time, it will affect the normal operation of the whole system. Based on this, an alarm data mining method of operation and maintenance support network based on streaming computing is proposed. The discrete alarm data is transformed into alarm transactions suitable for mining by sliding time window algorithm, and the transaction weight value is calculated by analytic hierarchy process; Spark framework and MapReduce framework are adopted to process data in batch, parallel and real-time, the alarm data mining of operation and maintenance support network is realised by setting a reasonable alarm event database threshold. Comparative experimental results show that the proposed method occupies the smallest memory and the shortest running time, and the performance of alarm data mining is the best.

Keywords: flow calculation; alarm data; spark framework; operation and maintenance support network; data mining.

DOI: 10.1504/IJDMB.2022.130341

International Journal of Data Mining and Bioinformatics, 2022 Vol.27 No.1/2/3, pp.13 - 26

Received: 03 Aug 2022
Accepted: 15 Dec 2022
Published online: 17 Apr 2023 *

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