Article

Data-driven validation, completion and construction of event relationship networks

Authors:

Joseph HellersteinAuthors Info & Claims

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 729 - 734

https://doi.org/10.1145/956750.956848

Published: 24 August 2003 Publication History

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Abstract

Event management is a focal point in building and maintaining high quality information infrastructures. We have witnessed the shift of the paradigm of event management in practice from root cause analysis (RCA) to action-oriented analysis (AOA). IBM has developed a pioneer event management methodology (EMD) based on the AOA paradigm and applied it to more than two hundred production sites with success. Foreseeably, more and more event management professionals will apply AOA in different incarnations in building proactive management facilities. By that, building correct and effective Event Relationship Networks (ERNs) becomes the dominating activity in AOA service design process. Currently, the quality of ERNs and the cost of building them largely depend on the knowledge of domain experts. We believe that we can utilize historical event logs in shortening the ERNs design process and perfecting the quality of ERNs. In this paper, we describe in detail how to apply this data-driven approach in ERN validation, completion and construction.

References

[1]

Burns, L., Hellerstein, J. L., Ma, S., Perng, C. -S., Rabenhorst, D. A. and Tayler, D., A Systematic Approach to Discovering Correlation Rules For Event Management, International Symposium on Integrated Network Management, 2001.]]

Google Scholar

[2]

Finkel, A., Houck, K., Calo, S., Bouloutas, A., An Alarm Correlation System for Heterogeneous Networks, Network Management and Control, Vol. 2, Plennum Press, 1994.]]

Google Scholar

[3]

J. L. Hellerstein and S. Ma. Mining event data for actionable patterns. International Conference for the resource management and performance evaluation of enterprise computing systems, 2000.]]

Google Scholar

[4]

Houck, K., Calo, S., Finkel, A., Towards a Practical Alarm Correlation System, Fourth International Symposium on Integrated Network management, Santa Barbara, CA, May, 1995.]]

Digital Library

Google Scholar

[5]

Jakobson, G., Weihmayer, R., Weissman, M., A Domain-Oriented Expert System Shell for Telecommunications Network Alarm Correlation, Network Management and Control, Vol. 2, Plennum Press, 1994.]]

Google Scholar

[6]

Kato, NeiOhta and Tomohiro KoheiIka, Proposal of Event Correlation for Distributed Network Fault Management and Its Evaluation, IEICE Transactions on Communications, Vol E92-B, number 6, 1999.]]

Google Scholar

[7]

Kliger, S., Yemini, S., Yemini, Y., Ohlse, D, Stolfo, S., A Coding Approach to Event Corrleation, Fourth International Symposium on Integrated Network Management, Santa Barbara, CA, May, 1995.]]

Digital Library

Google Scholar

[8]

Lewis, L., A Case-based Reasoning Approach to the Resolution of Faults in Communications Networks, in Proceedings Third International Symposium on Integrated Network Management, North Holland, 1993.]]

Digital Library

Google Scholar

[9]

Liu, G., Mok, A. K., and E. J. Yang., Composite Events for Network Event Correlation, International Symposium on Integrated Network Management, Boston, MA, May, 1999.]]

Crossref

Google Scholar

[10]

Ma., S., and Hellerstein, J. L., EventBrowser: A Flexible Tool for Scalable Analysis of Event Data. Distributed Operations and Management, 1999.]]

Digital Library

Google Scholar

[11]

Thoenen D., Riosa, J. And Hellerstein, J. L., Event Relationship Networks: A Framework for Action Oriented Analysis For Event Management, International Symposium on Integrated Network Management, 2001.]]

Google Scholar

Cited By

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Yang LYang N(2019)An Integrated Event Summarization Approach for Complex System ManagementIEEE Transactions on Network and Service Management10.1109/TNSM.2019.290619116:2(550-562)Online publication date: Jun-2019
https://doi.org/10.1109/TNSM.2019.2906191
Cao WLiu CHan Y(2019)Temporal Dependency Mining from Multi-sensor Event Sequences for Predictive MaintenanceWeb Information Systems and Applications10.1007/978-3-030-30952-7_27(257-269)Online publication date: 16-Sep-2019
https://doi.org/10.1007/978-3-030-30952-7_27
Shwartz LHwang JVölzer HNidd MAguiar MParaiso MValero L(2019)CEA: A Service for Cognitive Event AutomationService-Oriented Computing – ICSOC 2018 Workshops10.1007/978-3-030-17642-6_37(425-429)Online publication date: 10-Apr-2019
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Published In

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2003

736 pages

ISBN:1581137370

DOI:10.1145/956750

Conference Chair:
Lise Getoor
University of Maryland, College Park
,
General Chair:
Ted Senator
DARPA
,
Program Chairs:
Pedro Domingos
University of Washington
,
Christos Faloutsos
Carnegie Mellon University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2003

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KDD03

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KDD03: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2003

Washington, D.C.

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KDD '03 Paper Acceptance Rate 46 of 298 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Yang LYang N(2019)An Integrated Event Summarization Approach for Complex System ManagementIEEE Transactions on Network and Service Management10.1109/TNSM.2019.290619116:2(550-562)Online publication date: Jun-2019
https://doi.org/10.1109/TNSM.2019.2906191
Cao WLiu CHan Y(2019)Temporal Dependency Mining from Multi-sensor Event Sequences for Predictive MaintenanceWeb Information Systems and Applications10.1007/978-3-030-30952-7_27(257-269)Online publication date: 16-Sep-2019
https://doi.org/10.1007/978-3-030-30952-7_27
Shwartz LHwang JVölzer HNidd MAguiar MParaiso MValero L(2019)CEA: A Service for Cognitive Event AutomationService-Oriented Computing – ICSOC 2018 Workshops10.1007/978-3-030-17642-6_37(425-429)Online publication date: 10-Apr-2019
https://doi.org/10.1007/978-3-030-17642-6_37
Wu FAnchuri PLi ZMatwin SYu SFarooq F(2017)Structural Event Detection from Log MessagesProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098124(1175-1184)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098124
zeng cTang LZhou WLi TShwartz LGrabarnik G(2016)An Integrated framework for Mining Temporal Logs from Fluctuating EventsIEEE Transactions on Services Computing10.1109/TSC.2016.2598747(1-1)Online publication date: 2016
https://doi.org/10.1109/TSC.2016.2598747
Zhou WTang LZeng CLi TShwartz LYa. Grabarnik G(2016)Resolution Recommendation for Event Tickets in Service ManagementIEEE Transactions on Network and Service Management10.1109/TNSM.2016.258780713:4(954-967)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1109/TNSM.2016.2587807
Zhou WLi TShwartz LGrabarnik G(2015)Recommending ticket resolution using feature adaptationProceedings of the 2015 11th International Conference on Network and Service Management (CNSM)10.1109/CNSM.2015.7367333(15-21)Online publication date: 9-Nov-2015
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Zeng CTang LLi TShwartz LGrabarnik G(2014)Mining temporal lag from fluctuating events for correlation and root cause analysis10th International Conference on Network and Service Management (CNSM) and Workshop10.1109/CNSM.2014.7014137(19-27)Online publication date: Nov-2014
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Tang LLi TShwartz LPinel FGrabarnik GGrossman RUthurusamy RDhillon IKoren Y(2013)An integrated framework for optimizing automatic monitoring systems in large IT infrastructuresProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2488209(1249-1257)Online publication date: 11-Aug-2013
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Tang LLi TShwartz LGrabarnik G(2013)Identifying missed monitoring alerts based on unstructured incident ticketsProceedings of the 9th International Conference on Network and Service Management (CNSM 2013)10.1109/CNSM.2013.6727825(143-146)Online publication date: Oct-2013
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