AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction

Salman Ahmed; Muskaan Singh; Brendan Doherty; Effirul Ramlan; Kathryn Harkin; Damien Coyle

doi:10.1109/ISCMI56532.2022.10068482

AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction

Salman Ahmed, Muskaan Singh, Brendan Doherty, Effirul Ramlan, Kathryn Harkin, Damien Coyle

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

2 Citations (SciVal)

Abstract

The advancement of Artificial Intelligence has led to a surge in its application in Information Technology (IT) Operations, often termed Artificial Intelligence for IT Operations (AIOPS). One of the most challenging problems in AIOPS is IT Service Management (ITSM), which deals with incidents and anomalies of users, often referred to as tickets. These tickets are resolved by the IT firm support system, which plays a significant role in the company's user experiences, productivity, and profit. Recent advances have been made to automate the prediction of IT incidents and resolve them in a minimal time, utilizing AI models. In this paper, we take stock of the work in this domain and review the challenges. We also highlight the open topics that require further investigation for the advancement of the field.

Original language	English
Title of host publication	International Conference on Soft Computing & Machine Intelligence
Place of Publication	United States
Publisher	IEEE Computational Intelligence Society
Pages	253-257
Number of pages	5
DOIs	https://doi.org/10.1109/ISCMI56532.2022.10068482
Publication status	Published - 21 Mar 2023
Event	12022 9th International Conference on Soft Computing & Machine Intelligence - Toronto, Canada Duration: 26 Nov 2022 → 27 Nov 2022 https://ieeexplore.ieee.org/xpl/conhome/10068346/proceeding

Publication series

Name	2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Publisher	IEEE Control Society
ISSN (Print)	2640-0154
ISSN (Electronic)	2640-0146

Conference

Conference	12022 9th International Conference on Soft Computing & Machine Intelligence
Abbreviated title	ISCMI
Country/Territory	Canada
City	Toronto
Period	26/11/22 → 27/11/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10068346/proceeding

Bibliographical note

We are grateful for access to the Tier 2 High-Performance Computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. Damien Coyle is supported by the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). Salman Ahmed is supported by a Dr. George Moore Ph.D. scholarship.

Keywords

Artificial Intelligence for IT Operations (AIOPS)
IT Incidents
Risk prediction
Dataset Imbalance
IT Service Management (ITSM)
Information Technology Infrastructure Library (ITIL)

Access to Document

10.1109/ISCMI56532.2022.10068482

https://pure.ulster.ac.uk/en/publications/ai-for-information-technology-operation-aiops-a-review-of-it-inci

Cite this

Ahmed, S., Singh, M., Doherty, B., Ramlan, E., Harkin, K., & Coyle, D. (2023). AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction. In International Conference on Soft Computing & Machine Intelligence (pp. 253-257). (2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)). IEEE Computational Intelligence Society. https://doi.org/10.1109/ISCMI56532.2022.10068482

AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction. / Ahmed, Salman; Singh, Muskaan; Doherty, Brendan et al.
International Conference on Soft Computing & Machine Intelligence. United States: IEEE Computational Intelligence Society, 2023. p. 253-257 (2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Ahmed, S, Singh, M, Doherty, B, Ramlan, E, Harkin, K & Coyle, D 2023, AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction. in International Conference on Soft Computing & Machine Intelligence. 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE Computational Intelligence Society, United States, pp. 253-257, 12022 9th International Conference on Soft Computing & Machine Intelligence, Toronto, Canada, 26/11/22. https://doi.org/10.1109/ISCMI56532.2022.10068482

Ahmed S, Singh M, Doherty B, Ramlan E, Harkin K, Coyle D. AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction. In International Conference on Soft Computing & Machine Intelligence. United States: IEEE Computational Intelligence Society. 2023. p. 253-257. (2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)). doi: 10.1109/ISCMI56532.2022.10068482

Ahmed, Salman ; Singh, Muskaan ; Doherty, Brendan et al. / AI for Information Technology Operation (AIOps) : A review of IT Incident Risk Prediction. International Conference on Soft Computing & Machine Intelligence. United States : IEEE Computational Intelligence Society, 2023. pp. 253-257 (2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)).

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abstract = "The advancement of Artificial Intelligence has led to a surge in its application in Information Technology (IT) Operations, often termed Artificial Intelligence for IT Operations (AIOPS). One of the most challenging problems in AIOPS is IT Service Management (ITSM), which deals with incidents and anomalies of users, often referred to as tickets. These tickets are resolved by the IT firm support system, which plays a significant role in the company's user experiences, productivity, and profit. Recent advances have been made to automate the prediction of IT incidents and resolve them in a minimal time, utilizing AI models. In this paper, we take stock of the work in this domain and review the challenges. We also highlight the open topics that require further investigation for the advancement of the field.",

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AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this