Document Type



Doctor of Philosophy (PhD)


Computer Science

First Advisor's Name

Tao Li

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Shu-Ching Chen

Second Advisor's Committee Title

Committee co-chair

Third Advisor's Name

S. S. Iyengar

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Jaime Leonardo Bobadilla

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Wensong Wu

Fifth Advisor's Committee Title

Committee member


IT Service Management, Temporal Dependency, IT Ticket Classification, Incident Event, Time Series

Date of Defense



More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure.

Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automation operation are intertwined. Often the system administrators' roles are limited to helping triage tickets to the processing teams for problem resolving. The processing teams are responsible to perform a complex root cause analysis, providing the system statistics, event and ticket data. A large scale of system statistics, event and ticket data aggravate the burden of problem diagnosis on both the system administrators and the processing teams during routine maintenance procedures.

Alleviating human efforts involved in IT service management dictates intelligent and efficient solutions to maximize the automation of routine maintenance procedures. Three research directions are identified and considered to be helpful for IT service management optimization: (1) Automatically determine problem categories according to the symptom description in a ticket; (2) Intelligently discover interesting temporal patterns from system events; (3) Instantly identify temporal dependencies among system performance statistics data. Provided with ticket, event, and system performance statistics data, the three directions can be effectively addressed with a data-driven solution. The quality of IT service delivery can be improved in an efficient and effective way.

The dissertation addresses the research topics outlined above. Concretely, we design and develop data-driven solutions to help system administrators better manage the system and alleviate the human efforts involved in IT Service management, including (1) a knowledge guided hierarchical multi-label classification method for IT problem category determination based on both the symptom description in a ticket and the domain knowledge from the system administrators; (2) an efficient expectation maximization approach for temporal event pattern discovery based on a parametric model; (3) an online inference on time-varying temporal dependency discovery from large-scale time series data.





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