Course Title : Introduction to Process Mining

Course Code :  ECS 335/ECS 685

Learning Objectives : Process Mining provides a new means to discover, monitor and improve processes in a variety of application areas. It bridges the gap between data-driven approaches (e.g., data mining, machine learning) and model-based approaches (e.g., process management, simulation, formal methods). It provides fact-based insights which can be used to improve the operational processes themselves rather than the artifacts (models and data). Some example application domains are as follows: healthcare, automotive, electronics, transportation, telecom, aviation, pharmaceutical. The aim of this course is to familiarize students with the key concepts, ideas and techniques used in process mining.     

 After taking this course students are expected to  

• have a good understanding of the fundamental process mining techniques and tools

• apply basic process discovery and conformance checking techniques

• relate process mining techniques to other data and model-based analysis techniques like machine learning, data science, simulation, and formal methods

• use process mining techniques for prediction, recommendation and identifying bottlenecks in the system   

  Course Contents :

Module 1 : Preliminaries - Process Models, Model Based Process Analysis, Decision trees, Association Rule Mining, Clustering

Module 2 : Process Discovery - Data sources, Event logs, Alpha algorithm and its limitations, Representational bias, Noise & Incompleteness, Quality of discovered models

Module 3 : Advanced Process Discovery - Heuristic mining, Region based mining, Inductive mining

Module 4 : Conformance Checking – Comparing Footprints, Token replay, Alignments, Other applications of conformance checking

Module 5 : Mining Additional Perspectives – Organizational mining, Time & Probabilities, Decision mining

Module 6 : Big Event Data & Process Mining Software Support – Mining tools, Open-source platform, Commercial software, Case & activity-based decomposition                              

Selected Readings :

1) Process Mining: Data Science in Action, Springer-Verlag, 2016 (Second edition), Wil M. P. van der Aalst.

2) Process Mining in Action: Principles, Use Cases and Outlook, Springer-Verlag, 2020, Lars Reinkemeyer (Editor)