IT Support Conversation Manager: A Conversation-Centered Approach and Tool for Managing Best Practice IT Processes

Motahari-Nezhad, Hamid Reza; Bartolini, Claudio; Graupner, Sven; Singhal, Sharad; Spence, Susan

doi:10.1109/edoc.2010.20

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…A task is a business activity that is either predefined (part of a best practice process) or created on-the-fly by participants in a conversation [7]. We represent a task as T and define it as T(TASK PERFORMER, BENEFICIARY, action).…”

Section: A Taskmentioning

confidence: 99%

Monitoring Commitments in People-Driven Service Engagements

Kalia

Motahari-Nezhad

Bartolini

et al. 2013

2013 IEEE International Conference on Services Computing

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Abstract-People-driven service engagements involve communication over channels such as chat and email. Such engagements should be understood at the level of the commitments that the participants create and manipulate. Doing so provides a grounding for the communications and yields a business-level accounting of the progress of a service engagement. Existing work on commitment-based service engagements is limited to designtime model creation and verification. In contrast, we present a novel approach for capturing commitment-based engagements that are created dynamically in conversations. We monitor commitments identifying their creation, delegation, completion, or cancellation in the conversations. We have developed a prototype and evaluated it on real-world chat and email datasets. Our prototype captures commitments with a high F-measure of 90% in emails (Enron email corpus) and 80% in chats (HP IT support chat dataset) and provides promising results for capturing additional commitment operations.

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Section: A Taskmentioning

confidence: 99%

Monitoring Commitments in People-Driven Service Engagements

Kalia

Motahari-Nezhad

Bartolini

et al. 2013

2013 IEEE International Conference on Services Computing

Self Cite

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“…To pursue the aforementioned goal, we remark the use of AI as an enabler of smarter service continuity management. 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%

“…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 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.…”

mentioning

confidence: 99%

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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“…Motahari-Nezhad et al [93] first proposed a conversation management framework as a container for communication histories among support engineers in resolving the ticket. Next, the sequence of communication history can be extracted to model the routing steps between engineers [94].…”

Section: Using Routing Path and Ticket Textmentioning

confidence: 99%

Mining software support tickets for assistive routing

Han¹

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for sparing the time for discussions and reviewing my manuscripts. During my years studying in Nanyang Technological University of Singapore, I have the pleasure getting acquainted with many awesome people. Many of them are established researchers working in different fields, I wish them all the success and happiness in life.At SAP, I would like to thank my managers Steve Lee, Dr. Daniel Dahlmeier, and Dr. Chen Wang, for the careers guidance. A special regard to my first mentor Naveen Nandan, who introduced me to the company, and to Dr. Mohammad Akbari for his advice in research. For this special project on support tickets, I am always grateful to Andreas Heckmann, who envisioned an intelligent support application, and Christian Boos, who recommended me for the assignment. Since the beginning of the project, I worked closely with Project Manager Wilhelm Juette to form a productive team, his mentorship and friendship are always appreciated. Moreover, I wish to thank David Molinera, Kazuhiro Inaba and Ka Hian Goh for their hands-on support in the experiments.Most importantly, no word can describe my love and appreciation to my family, who are always there for me. Lastly, to my daughter. Learning is a lifelong journey, always be ready to embrace what is coming next.

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IT Support Conversation Manager: A Conversation-Centered Approach and Tool for Managing Best Practice IT Processes

Cited by 12 publications

References 8 publications

Monitoring Commitments in People-Driven Service Engagements

Monitoring Commitments in People-Driven Service Engagements

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

Mining software support tickets for assistive routing

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