Year

2018

Degree Name

Doctor of Philosophy

Department

School of Computing and Information Technology

Abstract

Business process provisioning involves the allocation of resources (people, technology, or information) to process tasks in order to optimally realize the goals of the process. Resource allocation or task allocation refers to matching the right resource(s) to a task. The allocation of resources to process tasks can have a significant impact on the performance (such as cost, time) of those tasks, and hence of the overall process. While the problem of optimal process provisioning is hard, process execution logs or event logs contain rich information about the task, resource and process outcome. Past resource allocation decisions, when correlated with process execution histories annotated with quality of service (or performance) measures, can be a rich source of knowledge about the best resource allocation decisions. This dissertation offers a number of different approaches to support data-driven business process provisioning.

In complex and knowledge intensive processes and services, human process participants (resources) often play a critical role. Process execution data from a range of sources suggest that human workers with the same organizational role and capabilities can have heterogeneous efficiencies based on their operational context. This dissertation investigates the variation in resource efficiencies with varying case attributes (or process instance attributes), using a log of past execution histories as the evidence base, also demonstrating how data-driven techniques can serve as the basis for methodological guidelines for effective dispatching and staffing policies required to meet the contractual service levels (quality) of the service system and the business process.

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