Multi-dimensional Knowledge Integration for Efficient Incident Management in a Services Cloud

Gupta, Rajeev; Prasad, Kalika; Luan, Laura; Rosu, Daniela; Ward, Chris

doi:10.1109/scc.2009.48

Cited by 25 publications

(12 citation statements)

References 2 publications

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“…In the service domain, process, and infrastructure were typically undermined and focused on macro-level (Jain & McLean;. In this new queue fallout management procedure, the timely diagnosis of the escalated incidents or problem Gupta, Prasad, Luan, Rosu & Ward (2009) to the WSG queue was highly regarded. The rudiments of incident management are to classify, prioritize those incident escalations and quick-ixed were applied Bartolini, Sallé& Trastour (2006) and Gupta, Prasad & Mohania (2008) with high consideration of its impact on service, customers, and business as well as urgency.…”

Section: Process Improvement For Queue Fallout Managementmentioning

confidence: 99%

A frontier in organizational and business process innovation in service management through lean six sigma Kaizen project implementation

2017

J. adm. bus. stud.

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Abstract. The rudiments and repercussions of the transformative global economy have reshaped the ServiceManagement industry and other developed economies. Business process innovation using Lean Six Sigma (LSS) methodology has long been considered to improve service quality, process ef iciency, and organizational performance by deploying Kaizen projects. This paper presents the business process innovation using De ine, Measure, Analyze, Improve and Control (DMAIC) methodology. The results show that the Kaizen Project positively impacted the company's productivity and support ef iciency and exceeded Service Level Agreement (SLA). The researcher recommends that the organization should continuously embark on process improvement, document lessons learned, share best practices, and champion LSS projects in larger scale and support operational units in institutionalizing the same innovation. Further studies could be done on the basis of how one-time scheme process better the quality of the service or product by using other framework and process improvement methodology.

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Section: Process Improvement For Queue Fallout Managementmentioning

confidence: 99%

A frontier in organizational and business process innovation in service management through lean six sigma Kaizen project implementation

2017

J. adm. bus. stud.

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“…In a simpler approach, Gupta et al [3] solely employ textual analysis techniques [4] to prepare incident tickets for classification, enrichment, and easier diagnosis. Their well-articulated system integrates numerous sources of information, but heavily leverages expert knowledge, human effort, and rule-based automation, which makes it difficult to implement and maintain.…”

Section: B Related Workmentioning

confidence: 99%

“…The Information Technology Infrastructure Library (ITIL) covers best practices in incident and problem management and previous work explores automated ticket escalation [3]. By contrast, event management still leverages legacy practices such as manual ticket analysis and correlation solely to lower redundancy.…”

Section: A Problem Contextmentioning

confidence: 99%

“…Alas, these are not available here. On the one hand, the support teams used for classes in [2] and [3] have a broader scope than just one root cause. On the other hand, one cannot learn root causes as class labels because this information is only documented for the rare cases in problem management.…”

Section: B Solution Overviewmentioning

confidence: 99%

“…Since hosts live on a network graph, network distance constitutes a more principled measure than merely (3). Assuming that most causes have small spread, the likelihood that events generated by different hosts have the same cause decreases with the distance between host vertices.…”

Section: B Feature Similaritymentioning

confidence: 99%

See 2 more Smart Citations

Clustering IT Events around Common Root Causes

Carjeu

Shorrock

Seeger

2014

2014 IEEE International Conference on Services Computing

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This paper focuses on clustering alerts around common root causes at the lower levels of the event management chain. The aim is to enable root-cause identification from a mixed event stream and to offer aggregated information for holistic problem solving. This end-to-end investigation spans feature selection and similarity assessment, clustering on heterogeneous feature maps, and evaluation of results. We compare feature values based on network information, userdefined similarity matrices, and textual analysis, and capture aspects of feature correlation in the event similarity function. Spectral clustering partitions the stream and serves to learn a more general similarity metric from a reference partitioning. Finally, we introduce two novel result visualization techniques and make a case study on one identified root-cause for which this framework outperforms both a time-pressured human operator and baseline clustering algorithms.

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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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Multi-dimensional Knowledge Integration for Efficient Incident Management in a Services Cloud

Cited by 25 publications

References 2 publications

A frontier in organizational and business process innovation in service management through lean six sigma Kaizen project implementation

A frontier in organizational and business process innovation in service management through lean six sigma Kaizen project implementation

Clustering IT Events around Common Root Causes

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

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