research-article

On Automating XSEDE User Ticket Classification

Authors:

Victor Hazlewood,

Gregory D. PetersonAuthors Info & Claims

XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment

Article No.: 41, Pages 1 - 7

https://doi.org/10.1145/2616498.2616549

Published: 13 July 2014 Publication History

Abstract

The XSEDE ticket system, which is a help desk ticketing system, receives email and web-based problem reports (i.e., tickets) from users and these tickets can be manually grouped into predefined categories either by the ticket submitter or by operations staff. This manual process can be automated by using text classification algorithms such as Multinomial Naive Bayes (MNB) or Softmax Regression Neural Network (SNN). Ticket subjects, rather than whole tickets, were used to make an input word list along with a manual word group list to enhance accuracy. The text mining algorithms used the input word list to select input words in the tickets. Compared with the Matlab svm() function, MNB and SNN showed overall better accuracy (up to ~85.8% using two simultaneous category selection). Also, the service provider resource (i.e., system name) information could be extracted from the tickets with ~90% accuracy.

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Cited By

Batra RS VPatil S(2024)Enhancement of 5G N/W System for the use of ML Algorithm Based Ticket-Reopening System for the use of Attack Prediction2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS)10.1109/ISTEMS60181.2024.10560321(1-5)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ISTEMS60181.2024.10560321
Kuporenkov VMokhov ABobryakov AGavrilov ANaryadkina N(2022)Approach to Develop an Intelligent User Support System of the Educational Platform2022 VI International Conference on Information Technologies in Engineering Education (Inforino)10.1109/Inforino53888.2022.9782934(1-4)Online publication date: 12-Apr-2022
https://doi.org/10.1109/Inforino53888.2022.9782934
Swain AGarza V(2022)Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident ResolutionInformation Systems Frontiers10.1007/s10796-022-10266-5Online publication date: 23-Mar-2022
https://doi.org/10.1007/s10796-022-10266-5
Show More Cited By

Index Terms

On Automating XSEDE User Ticket Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment

July 2014

445 pages

ISBN:9781450328937

DOI:10.1145/2616498

General Chair:
Scott Lathrop
National Center for Supercomputing Applications
,
Program Chair:
Jay Alameda
National Center for Supercomputing Applications

Copyright © 2014 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]

In-Cooperation

NSF: National Science Foundation
Drexel University
Indiana University: Indiana University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2014

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XSEDE '14

XSEDE '14: Annual Conference of the Extreme Science and Engineering Discovery Environment

July 13 - 18, 2014

GA, Atlanta, USA

Acceptance Rates

XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;

Overall Acceptance Rate 129 of 190 submissions, 68%

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Cited By

Batra RS VPatil S(2024)Enhancement of 5G N/W System for the use of ML Algorithm Based Ticket-Reopening System for the use of Attack Prediction2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS)10.1109/ISTEMS60181.2024.10560321(1-5)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ISTEMS60181.2024.10560321
Kuporenkov VMokhov ABobryakov AGavrilov ANaryadkina N(2022)Approach to Develop an Intelligent User Support System of the Educational Platform2022 VI International Conference on Information Technologies in Engineering Education (Inforino)10.1109/Inforino53888.2022.9782934(1-4)Online publication date: 12-Apr-2022
https://doi.org/10.1109/Inforino53888.2022.9782934
Swain AGarza V(2022)Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident ResolutionInformation Systems Frontiers10.1007/s10796-022-10266-5Online publication date: 23-Mar-2022
https://doi.org/10.1007/s10796-022-10266-5
Ricciardi Celsi LCaliciotti AD'Onorio MScocchi ESulieman NVillari M(2021)On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic NetworksFuture Internet10.3390/fi1310025913:10(259)Online publication date: 9-Oct-2021
https://doi.org/10.3390/fi13100259
YANG L(2021)Fuzzy Output Support Vector Machine Based Incident Ticket ClassificationIEICE Transactions on Information and Systems10.1587/transinf.2020EDP7044E104.D:1(146-151)Online publication date: 1-Jan-2021
https://doi.org/10.1587/transinf.2020EDP7044
Pidej CThaicharoen S(2021)Bilingual IT Service Desk Ticket Classification Using Language Model Pre-training Techniques2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)10.1109/iSAI-NLP54397.2021.9678179(1-6)Online publication date: 21-Dec-2021
https://doi.org/10.1109/iSAI-NLP54397.2021.9678179
Wahba YMadhavji NSteinbacher J(2020)Evaluating the effectiveness of static word embeddings on the classification of IT support ticketsProceedings of the 30th Annual International Conference on Computer Science and Software Engineering10.5555/3432601.3432628(198-206)Online publication date: 10-Nov-2020
https://dl.acm.org/doi/10.5555/3432601.3432628
Al-Hawari FBarham H(2019)A Machine Learning Based Help Desk System for IT Service ManagementJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.04.001Online publication date: Apr-2019
https://doi.org/10.1016/j.jksuci.2019.04.001
Xu JZhang HZhou WHe RLi T(2018)Signature based trouble ticket classificationFuture Generation Computer Systems10.1016/j.future.2017.07.05478:P1(41-58)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.07.054
Paramesh SShreedhara K(2018)Automated IT Service Desk Systems Using Machine Learning TechniquesData Analytics and Learning10.1007/978-981-13-2514-4_28(331-346)Online publication date: 5-Nov-2018
https://doi.org/10.1007/978-981-13-2514-4_28

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