Event summarization for system management

Peng, Wei; Perng, Chang-Shing; Li, Tao; Wang, Haixun

doi:10.1145/1281192.1281305

Cited by 31 publications

(18 citation statements)

References 9 publications

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“…Before directly diving into the details, it is a good choice for the analysts to see the summary first. Peng et al [14] proposed an approach to find the dependency among events by measuring inter-arrival distribution of the event. Kiernan et al [8] summarized the events by segmenting the event sequence according to the frequency changes.…”

Section: Related Workmentioning

confidence: 99%

META: Multi-resolution Framework for Event Summarization

Jiang

Perng

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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Event summarization is an effective process that mines and organizes event patterns to represent the original events. It allows the analysts to quickly gain the general idea of the events. In recent years, several event summarization algorithms have been proposed, but they all focus on how to find out the optimal summarization results, and are designed for one-time analysis. As event summarization is a comprehensive analysis work, merely handling this problem with a single optimal algorithm is not enough.In the absence of an integrated summarization solution, we propose an extensible framework -META -to enable analysts to easily and selectively extract and summarize events from different views with different resolutions. In this framework, we store the original events in a carefully-designed data structure that enables an efficient storage and multiresolution analysis. On top of the data model, we define a summarization language that includes a set of atomic operators to manipulate the meta-data. Furthermore, we present 5 commonly used summarization tasks, and show that all these tasks can be easily expressed by the language. Experimental evaluation on both real and synthetic datasets demonstrates the efficiency and effectiveness of our framework.

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Section: Related Workmentioning

confidence: 99%

META: Multi-resolution Framework for Event Summarization

Jiang

Perng

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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“…See example, [13], [21], [16], [32]. Different types of patterns, such as (partially) periodic patterns, event bursts, and mutually dependent patterns were introduced to describe system management events.…”

Section: Related Workmentioning

confidence: 99%

An integrated framework for optimizing automatic monitoring systems in large IT infrastructures

Tang

Shwartz

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The competitive business climate and the complexity of IT environments dictate efficient and cost-effective service delivery and support of IT services. These are largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides an effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes an integrated framework for minimizing false positive tickets and maximizing the monitoring coverage for system faults.In particular, the integrated framework defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems. Moreover, a part of misconfigured monitoring conditions can be corrected according to false negative tickets that are discovered by another text classification algorithm in this framework. This paper also provides implementation details of a program product that uses this framework and shows some illustrative examples of successful results.

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“…These frequent pattern mining techniques can reveal some interesting patterns by identifying the correlations of discrete events and are the building blocks of event summarization. Event summarization has attracted a lot of research attention recently [14,15,25,24]. Peng et al [24] proposed an approach to find the patterns in the event logs by measuring inter-arrivals of the events.…”

Section: Related Workmentioning

confidence: 99%

“…Event summarization has attracted a lot of research attention recently [14,15,25,24]. Peng et al [24] proposed an approach to find the patterns in the event logs by measuring inter-arrivals of the events. Kiernan et al [14,15] proposed a summarization method by segmenting the event sequence according to the frequency changes of events.…”

Section: Related Workmentioning

confidence: 99%

Natural event summarization

Jiang

Perng²,

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as ignoring temporal information, generating the same set of boundaries for all event patterns, and providing a summary which is difficult for human to understand.In this paper, we propose a novel framework called natural event summarization that summarizes an event sequence using inter-arrival histograms to capture the temporal relationship among events. Our framework uses the minimum description length principle to guide the process in order to balance between accuracy and brevity. Also, we use multi-resolution analysis for pruning the problem space. We demonstrate how the principles can be applied to generate summaries with periodic patterns and correlation patterns in the framework. Experimental results on synthetic and real data show our method is capable of producing usable event summary, robust to noises, and scalable.

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Event summarization for system management

Cited by 31 publications

References 9 publications

META: Multi-resolution Framework for Event Summarization

META: Multi-resolution Framework for Event Summarization

An integrated framework for optimizing automatic monitoring systems in large IT infrastructures

Natural event summarization

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