research-article

Recommending resolutions of ITIL services tickets using Deep Neural Network

Authors:

Durga Prasad Muni,

Yeung Tack Yan John John Lew Chiang,

Antoine Jean-Marie Viallet,

Navin BudhirajaAuthors Info & Claims

CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

Article No.: 14, Pages 1 - 10

https://doi.org/10.1145/3041823.3041831

Published: 09 March 2017 Publication History

Abstract

Application development and maintenance is a good example of Information Technology Infrastructure Library (ITIL) services in which a sizable volume of tickets are raised everyday for different issues to be resolved in order to deliver uninterrupted service. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. It will be beneficial to automatically extract information from the description of tickets to improve operations like identifying critical and frequent issues, grouping of tickets based on textual content, suggesting remedial measures for them etc. In particular, the maintenance people can save a lot of effort and time if they have access to past remedial actions for similar kind of tickets raised earlier based on history data. In this work we propose an automated method based on deep neural networks for recommending resolutions for incoming tickets. We use ideas from deep structured semantic models (DSSM) for web search for such resolution recovery. We project a small subset of existing tickets in pairs and an incoming ticket to a low dimensional feature space, following which we compute the similarity of an existing ticket with the new ticket. We select the pair of tickets which has the maximum similarity with the incoming ticket and publish both of its resolutions as the suggested resolutions for the latter ticket. The experiment of our data sets shows that we are able to achieve a promising similarity match of about 70% - 90% between the suggestions and the actual resolution.

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Cited By

Subbarao MVenkatarao KChittineni SRamana S(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
https://doi.org/10.1007/978-981-99-7137-4_34
Pereira LPizzio RBonho SDe Souza LJunior A(2023)Machine Learning for Classification of IT Support Tickets2023 International Conference On Cyber Management And Engineering (CyMaEn)10.1109/CyMaEn57228.2023.10051041(210-213)Online publication date: 26-Jan-2023
https://doi.org/10.1109/CyMaEn57228.2023.10051041
Ahmed SSingh MDoherty BRamlan EHarkin KBucholc MCoyle D(2023)Knowledge-based Intelligent System for IT Incident DevOps2023 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps)10.1109/AIOps59134.2023.00005(1-7)Online publication date: May-2023
https://doi.org/10.1109/AIOps59134.2023.00005
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cover image ACM Other conferences

CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

March 2017

136 pages

ISBN:9781450348461

DOI:10.1145/3041823

Copyright © 2017 ACM.

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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SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Amazon: Amazon.com

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2017

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CODS '17

CODS '17: Fourth ACM IKDD Conferences on Data Sciences

March 9 - 11, 2017

Chennai, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

Subbarao MVenkatarao KChittineni SRamana S(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
https://doi.org/10.1007/978-981-99-7137-4_34
Pereira LPizzio RBonho SDe Souza LJunior A(2023)Machine Learning for Classification of IT Support Tickets2023 International Conference On Cyber Management And Engineering (CyMaEn)10.1109/CyMaEn57228.2023.10051041(210-213)Online publication date: 26-Jan-2023
https://doi.org/10.1109/CyMaEn57228.2023.10051041
Ahmed SSingh MDoherty BRamlan EHarkin KBucholc MCoyle D(2023)Knowledge-based Intelligent System for IT Incident DevOps2023 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps)10.1109/AIOps59134.2023.00005(1-7)Online publication date: May-2023
https://doi.org/10.1109/AIOps59134.2023.00005
Ali Zaidi SFraz MShahzad MKhan S(2021)A multiapproach generalized framework for automated solution suggestion of support ticketsInternational Journal of Intelligent Systems10.1002/int.2270137:6(3654-3681)Online publication date: 30-Sep-2021
https://doi.org/10.1002/int.22701
Molino PZheng HWang YGuo YFarooq F(2018)COTAProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219851(586-595)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219851
Kubiak PRass S(2018)An Overview of Data-Driven Techniques for IT-Service-ManagementIEEE Access10.1109/ACCESS.2018.28759756(63664-63688)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2875975

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