教學大綱 Syllabus

課程簡介Course Description

Process mining is a recent technology that combines data science and business process analysis. Unlike traditional business process management, which relies on workshops and interviews to create an idealized representation of a process, process mining uses data mining methods to generate a depiction of the process. Particularly, process mining produces and analyzes process models solely based on event logs recorded by corporate information systems. By inspecting these event logs, managers and analysts can obtain novel insights to address performance and compliance problems of business processes.

This course introduces the fundamentals of process mining, enabling students to learn how these mining technologies work, when to apply them, and what their outcomes may be. We will cover three categories of analysis methods: (1) process discovery, which constructs a process model from an event log without a-priori information; (2) conformance checking, which checks if an existing process model conforms to the behavior of a business process and vice versa; (3) process enhancement, which improves a process model with additional information about the real process. All these categories provide data-centric techniques to support an analyst finding issues in business processes. We will also discuss process mining practices in application domains such as healthcare, financial auditing, and robotics. Finally, if time permits, we will introduce some useful mining tools developed in the literature.