Building IT-based incident management platform

Wang, Qian; Song, Junde; Liu, Lianru; Luo, Xiaoxiang; Xinhua, E

doi:10.1109/icpca.2010.5704127

Cited by 7 publications

(2 citation statements)

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“…Indeed, although IM approaches have been studied since the seventies, 7 the resolution of incident tickets is still mainly done manually, strongly based on analysts' experience and on their interactions with customers 8 . This means that this ticket‐resolution is still most time consuming and fallible 9,10 …”

Section: Introductionmentioning

confidence: 99%

“…8 This means that this ticket-resolution is still most time consuming and fallible. 9,10 To start mitigating the problem, the article presents ACQUA (standing for Automatic tiCket Quality Assessment), an approach based on machine-learning (ML) that aims to reduce-and eventually eliminate-the need of many customer-analyst iterations as well as completely automate the industrial systems' incident resolution and service continuity procedures. In the scope of the aforementioned concept, we propose the operationalization of the notion of software ticket quality within the ITIL framework 11 -the Information Technology Infrastructure Library, a library for advanced service management, governance, and operation-intended as the extent to which a service incident ticket conveys 100% of the information required to diagnose the incident itself.…”

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confidence: 99%

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Making service continuity smarter with artificial intelligence: An approach and its evaluation

2023

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Service continuity entails establishing an observable and explainable continuum between customer experience and service operations. Such continuum is currently established manually, via service customer management operations (such as service incident management (IM)) often resulting in time‐consuming, human‐detrimental, and error‐prone activities. Conversely, artificial intelligence (AI) is rapidly emerging as an automated enabler towards handling the discontinuities in the aforementioned critical business tasks. Consequently, the emerging topic of AI‐driven incident management (AIIM) addresses practices and tools to resolve incidents by means of AI‐enabled organizational processes and methodologies. Our conjecture is that AIIM could reduce unplanned interruptions of service and let customers resume their work as quick as possible. While several techniques were presented in the literature to automatically identify the problems described in incident tickets by customers, this article focuses on the qualitative analysis and feature extraction off of the provided descriptions. When an incident ticket does not describe properly the problem, the analyst must ask the customer for additional details which could require several long‐lasting interactions. This article proposes ACQUA , an AIIM approach to automatically assess the quality of ticket descriptions with the goals of removing the need of additional communications and guiding the customers to properly describe the incident. A preliminary evaluation of ACQUA was performed on a dataset provided by a large bank in Europe, showing promising results and a boost of 13% in ticket resolution times and connected service continuity.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%