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Published in:
2021 | OriginalPaper | Chapter
Research on Alarm Causality Filtering Based on Association Mining
Authors : Yuan Liu, Yi Man, Jianuo Cui
Published in: Human Centered Computing
Publisher: Springer International Publishing
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Abstract
Mining the association rules in the alarm data generated by network is an important method for operations to monitor and manage the network equipment. Analyzing the correlation of alarms through association rule mining algorithms can effectively simplify alarms and help locate network faults. Since the network alarm data has an obvious chronological relationship, it needs to be processed by the association rule mining algorithm based on time series. Through investigation, it is found that current association rule algorithms based on time series lack the determination of the realistic cause-and-effect relationship between successive alarms. Therefore, in order to improve the effectiveness of the association algorithm, this paper adopts an association mining algorithm based on the existing time series, which supports filtering the useless sequential associated items that have no causal relationship in the results. The experimental and analytical results show that the proposed method is effective.