2014 TowardsScalableCriticalAlertMin

• (Zong et al., 2014) ⇒ Bo Zong, Yinghui Wu, Jie Song, Ambuj K. Singh, Hasan Cam, Jiawei Han, and Xifeng Yan. (2014). “Towards Scalable Critical Alert Mining.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623729

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Towards+Scalable+Critical+Alert+Mining
• http://dl.acm.org/citation.cfm?id=2623330.2623729&preflayout=flat#citedby

Quotes

Author Keywords

• Critical alert mining; data center troubleshooting; data mining; graph mining; information filtering; root cause analysis; search process; selection process.

Abstract

Performance monitor software for data centers typically generates a great number of alert sequences. These alert sequences indicate abnormal network events. Given a set of observed alert sequences, it is important to identify the most critical alerts that are potentially the causes of others. While the need for mining critical alerts over large scale alert sequences is evident, most alert analysis techniques stop at modeling and mining the causal relations among the alerts.

This paper studies the critical alert mining problem: Given a set of alert sequences, we aim to find a set of k critical alerts such that the number of alerts potentially triggered by them is maximized. We show that the problem is intractable; therefore, we resort to approximation and heuristic algorithms. First, we develop an approximation algorithm that obtains a near-optimal alert set in quadratic time, and propose pruning techniques to improve its runtime performance. Moreover, we show a faster approximation exists, when the alerts follow certain causal structure. Second, we propose two fast heuristic algorithms based on tree sampling techniques. On real-life data, these algorithms identify a critical alert from up to 270,000 mined causal relations in 5 seconds; meanwhile, they preserve more than 80% of solution quality, and are up to 5,000 times faster than their approximation counterparts.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 TowardsScalableCriticalAlertMin	Xifeng Yan Bo Zong Yinghui Wu Jie Song Ambuj K. Singh Hasan Cam Jiawei Han			Towards Scalable Critical Alert Mining				10.1145/2623330.2623729		2014