國立虎尾科技大學 |

Augmenting Collective Expert Networks to Improve Service Level Compliance.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Augmenting Collective Expert Networks to Improve Service Level Compliance./
作者:	Moharreri, Kayhan.
面頁冊數:	1 online resource (167 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355661149

Augmenting Collective Expert Networks to Improve Service Level Compliance.
Moharreri, Kayhan.

Augmenting Collective Expert Networks to Improve Service Level Compliance. - 1 online resource (167 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--The Ohio State University, 2017.

Includes bibliographical references

This research introduces and develops the new subfield of large-scale collective expert networks (CEN) concerned with time-constrained triaging which has become critical to the delivery of increasingly complex enterprise services. The main research contribution augments existing human-intensive interactions in the CEN with models that use ticket content and transfer sequence histories to generate assistive recommendations. This is achieved with a recommendation framework that improves the performance of CEN by: (1) resolving incidents to meet customer time constraints and satisfaction, (2) conforming to previous transfer sequences that have already achieved their Service Levels; and additionally, (3) addressing trust to encourage adoption of recommendations. A novel basis of this research is the exploration and discovery of resolution process patterns, and leveraging them towards the construction of an assistive resolution recommendation framework. Additional interesting new discoveries regarding CENs include existence of resolution workflows and their frequent use to carry out service-level-effective resolution on regular content. In addition, the ticket-specific expertise of the problem solvers and their dynamic ticket load were found to be factors in the time taken to resolve an incoming ticket. Also, transfers were found to reflect the experts' local problem-solving intent with respect to the source and target nodes. The network performs well if certain transfer intents (such as resolution and collective) are exhibited more often than the others (such as mediation and exploratory).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355661149Subjects--Topical Terms:

559429
Information technology.
Index Terms--Genre/Form:

554714
Electronic books.

Augmenting Collective Expert Networks to Improve Service Level Compliance.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Jayashree Ramanathan.
502 $a Thesis (Ph.D.)--The Ohio State University, 2017.
504 $a Includes bibliographical references
520 $a This research introduces and develops the new subfield of large-scale collective expert networks (CEN) concerned with time-constrained triaging which has become critical to the delivery of increasingly complex enterprise services. The main research contribution augments existing human-intensive interactions in the CEN with models that use ticket content and transfer sequence histories to generate assistive recommendations. This is achieved with a recommendation framework that improves the performance of CEN by: (1) resolving incidents to meet customer time constraints and satisfaction, (2) conforming to previous transfer sequences that have already achieved their Service Levels; and additionally, (3) addressing trust to encourage adoption of recommendations. A novel basis of this research is the exploration and discovery of resolution process patterns, and leveraging them towards the construction of an assistive resolution recommendation framework. Additional interesting new discoveries regarding CENs include existence of resolution workflows and their frequent use to carry out service-level-effective resolution on regular content. In addition, the ticket-specific expertise of the problem solvers and their dynamic ticket load were found to be factors in the time taken to resolve an incoming ticket. Also, transfers were found to reflect the experts' local problem-solving intent with respect to the source and target nodes. The network performs well if certain transfer intents (such as resolution and collective) are exhibited more often than the others (such as mediation and exploratory).
520 $a The assistive resolution recommendation framework incorporates appropriate strategies for addressing the entire spectrum of incidents. This framework consists of a two-level classifier with the following parts: (1) content tagger for routine/non-routine classification, (2) A sequence classifier for resolution workflow recommendation, (3) Response time estimation based on learned dynamics of the CEN (i.e. Expertise, and ticket load), and (4) transfer intent identification. Our solution makes reliable proactive recommendations only in the case of adequate historical evidence thus helping to maintain a high level of trust with the interacting users in the CEN. By separating well-established resolution workflows from incidents that depend on experts' experiential and 'tribal' knowledge for the resolution, this research shows a 34% performance improvement over existing content-aware greedy transfer model; it is also estimated that there will be a 10% reduction in the volume of service-level breached tickets.
520 $a The contributions are shown to benefit the enterprise support and delivery services by providing (1) lower decision and resolution latency, (2) lower likelihood of service-level violations, and (3) higher workforce availability and effectiveness. More generally, the contributions of this research are applicable to a broad class of problems where time-constrained content-driven problem-solving by human experts is a necessity.
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538 $a Mode of access: World Wide Web
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650 4 $a Artificial intelligence. $3 559380
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