research-article

Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching

Authors:

Mirela Madalina Botezatu,

Jasmina Bogojeska,

Dorothea WiesmannAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1711 - 1720

https://doi.org/10.1145/2783258.2788607

Published: 10 August 2015 Publication History

Abstract

We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agent's speed from historical data into account. Our solution consists of two parts. First, a novel technique clusters historic tickets into incident categories that are discriminative in terms of agent's performance. Second, a dispatching policy selects, for an incoming ticket, the fastest available agent according to the target cluster. We show that, for ticket data collected from several Service Delivery Units, our new dispatching technique can reduce service time between $35\%$ and $44\%$.

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Cited By

Kalia ABatta RXiao JVukovic M(2021)Ensemble of Unsupervised Parametric and Non-Parametric Techniques to Discover Change Actions2021 IEEE 14th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD53861.2021.00074(572-577)Online publication date: Sep-2021
https://doi.org/10.1109/CLOUD53861.2021.00074
Xu JMu JChen G(2020)A multi-view similarity measure framework for trouble ticket miningData & Knowledge Engineering10.1016/j.datak.2020.101800127(101800)Online publication date: May-2020
https://doi.org/10.1016/j.datak.2020.101800
Mandal AAgarwal SMalhotra NSridhara GRay ASwarup D(2019)Improving IT Support by Enhancing Incident Management Process with Multi-modal AnalysisService-Oriented Computing10.1007/978-3-030-33702-5_33(431-446)Online publication date: 22-Oct-2019
https://doi.org/10.1007/978-3-030-33702-5_33
Show More Cited By

Index Terms

Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

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Publication History

Published: 10 August 2015

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

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

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Kalia ABatta RXiao JVukovic M(2021)Ensemble of Unsupervised Parametric and Non-Parametric Techniques to Discover Change Actions2021 IEEE 14th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD53861.2021.00074(572-577)Online publication date: Sep-2021
https://doi.org/10.1109/CLOUD53861.2021.00074
Xu JMu JChen G(2020)A multi-view similarity measure framework for trouble ticket miningData & Knowledge Engineering10.1016/j.datak.2020.101800127(101800)Online publication date: May-2020
https://doi.org/10.1016/j.datak.2020.101800
Mandal AAgarwal SMalhotra NSridhara GRay ASwarup D(2019)Improving IT Support by Enhancing Incident Management Process with Multi-modal AnalysisService-Oriented Computing10.1007/978-3-030-33702-5_33(431-446)Online publication date: 22-Oct-2019
https://doi.org/10.1007/978-3-030-33702-5_33
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
Xue JBirke RChen LSmirni E(2018)Spatial–Temporal Prediction Models for Active Ticket Managing in Data CentersIEEE Transactions on Network and Service Management10.1109/TNSM.2018.279440915:1(39-52)Online publication date: Mar-2018
https://doi.org/10.1109/TNSM.2018.2794409
Xu JHe RZhou WLi T(2018)Trouble Ticket Routing Models and Their ApplicationsIEEE Transactions on Network and Service Management10.1109/TNSM.2018.279095615:2(530-543)Online publication date: Jun-2018
https://doi.org/10.1109/TNSM.2018.2790956
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
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
Misra JSengupta SRawat DSavagaonkar MPodder SShukla RSen SBishop JBreitman K(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
https://dl.acm.org/doi/10.1109/SER-IP.2017..8
Liang SYilmaz EKanoulas EKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Dynamic Clustering of Streaming Short DocumentsProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939748(995-1004)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939748
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