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Final_Dissertation_Kayhan_Moharreri_OhioLink.pdf (7.24 MB)
ETD Abstract Container
Abstract Header
Augmenting Collective Expert Networks to Improve Service Level Compliance
Author Info
Moharreri, Kayhan
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1500649086849134
Abstract Details
Year and Degree
2017, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Abstract
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). 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. 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.
Committee
Jayashree Ramanathan (Advisor)
Rajiv Ramnath (Committee Member)
Srinivasan Parthasarathy (Committee Member)
Gagan Agrawal (Committee Member)
Pages
167 p.
Subject Headings
Artificial Intelligence
;
Computer Science
;
Information Science
;
Information Technology
Keywords
IT Service Management, Collective Expert Networks, Process Discovery, Ticket Routing Recommendations, Resolution Time Estimation, Event Mining, IT Service Support, Service Level Compliance, Human-in-the-loop, Learning from Enterprise Event Data
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Citations
Moharreri, K. (2017).
Augmenting Collective Expert Networks to Improve Service Level Compliance
[Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500649086849134
APA Style (7th edition)
Moharreri, Kayhan.
Augmenting Collective Expert Networks to Improve Service Level Compliance.
2017. Ohio State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1500649086849134.
MLA Style (8th edition)
Moharreri, Kayhan. "Augmenting Collective Expert Networks to Improve Service Level Compliance." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500649086849134
Chicago Manual of Style (17th edition)
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Document number:
osu1500649086849134
Download Count:
695
Copyright Info
© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.