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A knowledge-rich similarity measure for improving IT incident resolution process

Published: 22 March 2010 Publication History

Abstract

The aim of incident management is to restore a given IT service disruption, simply called incident, to normal state as quickly as possible. In incident management, it is essential to resolve a new incident efficiently and accurately. However, typically, incident resolution process is largely manual, thus, it is time-consuming and error-prone. This paper proposes a new knowledge-rich similarity measure for improving this process. The role of this measure is to retrieve the most similar past incident cases for a new incident without human intervention. The solution information contained the retrieved incident cases can be utilized to resolve the new incident. The main feature of our similarity measure is to incorporate additional useful meta knowledge, outside of incident description that is the only exploited information in typical similarity measures used in CBR, to improve effectiveness. Moreover, this measure exploits as much semantic knowledge as possible about features contained in previous incident cases. Through an experimental evaluation, we show the effectiveness, technical coherence and feasibility of this measure using a real dataset.

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      cover image ACM Conferences
      SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
      March 2010
      2712 pages
      ISBN:9781605586397
      DOI:10.1145/1774088
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      Published: 22 March 2010

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      Author Tags

      1. IT incident management
      2. IT service management
      3. incident resolution process
      4. knowledge-rich similarity measure

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      • (2024)Automated Dialogue-Based Response and Resolution of Conversational IT Tickets Using Deep Neural NetworksProceedings of the 6th International Conference on Communications and Cyber Physical Engineering10.1007/978-981-99-7137-4_34(351-366)Online publication date: 5-Feb-2024
      • (2018)Information Retrieval in Case Based Reasoning Using Vertical Association Knowledge and Shannon Information Gain2018 International Conference on Information , Communication, Engineering and Technology (ICICET)10.1109/ICICET.2018.8533793(1-4)Online publication date: Aug-2018
      • (2017)Data-driven application maintenanceProceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice10.1109/SER-IP.2017..8(48-54)Online publication date: 20-May-2017
      • (2015)Capturing Researcher Expertise through MeSH ClassificationProceedings of the 8th International Conference on Knowledge Capture10.1145/2815833.2815837(1-8)Online publication date: 7-Oct-2015
      • (2014)A Retrieval Strategy for Case-Based Reasoning Using Similarity and Association KnowledgeIEEE Transactions on Cybernetics10.1109/TCYB.2013.225774644:4(473-487)Online publication date: Apr-2014
      • (2012)CoKIMProceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)10.1109/ASONAM.2012.209(1211-1214)Online publication date: 26-Aug-2012
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      • (2011)Analytics for similarity matching of IT cases with collaboratively-defined activity flowsProceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops10.1109/ICDEW.2011.5767639(273-278)Online publication date: 11-Apr-2011
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