PhD Course on Process Mining

PhD in Engineering in Computer Science

Sapienza Università di Roma

Main Instructor: Prof. Fabrizio Maria Maggi

A.A. 2018/2019

Course Description

The course will provide an introduction to the key analysis techniques in process mining that allow users to automatically learn process models from raw event logs (process discovery) and to check if reality, as recorded in the event logs, conforms to the models and vice versa (conformance checking). In the context of process discovery and conformance checking, well-established techniques available in the literature will be presented, as well as more recent advances that are currently investigated in the process mining community, such as process mining with declarative specifications or BPMN. Other branches of the process mining field will also be presented like performance analysis, bottleneck detection, social network analysis. In addition, the course will discuss how recent techniques from academic artificial intelligence research can be employed to advance the state-of-the art in process mining. Finally the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

Learning Objectives

The learning objectives of this course are:
  • master the theoretical foundations underlying existing process mining techniques;
  • understand how to practically apply basic process discovery / conformance checking / enhancement techniques, both manually and using tools;
  • work with the process mining tools ProM and Disco to perform process mining analyses and correctly interpret the results;
  • know the most recent scientific advancements in process mining.

Prerequisites and Evaluation

This course is aimed at PhD students, Master students and professionals. A basic understanding of logic, set theory, and statistics (at the undergraduate level) is assumed. Basic computer skills are required to use the software provided by the course (but no programming experience is needed).
In order to get the credits provided by the course, a short project on process mining from the course participants (to be held individually or in group) is required. With the project, the participants must demonstrate the ability to put into practice the activities illustrated or carried out during the course.
Lectures Room Room B203 (lectures 1-4, second floor) and Room A7 (lecture 5, ground floor) Dipartimento di Ingegneria Informatica, Automatica e Gestionale (DIAG), Via Ariosto 25, Roma.
Main Reference Wil M.P. van der Aalst: Process Mining. Data Science in Action. Springer-Verlag Berlin Heidelberg, 2016
E-mail Address
  • A. Marrella: marrella <at> diag <dot> uniroma1 <dot> it
  • F.M. Maggi: f <dot> m <dot> maggi <at> ut <dot> ee

News

  • The slides of the course will be uploaded before the associated lectures.

Schedule of Lectures

DATE AND TIME TOPICS TACKLED
13/2/2019
10:00-14:00
  • Introduction to the course
    • Outline of the lectures
  • Basics of Process Mining
    • Process Mining in the context of Data Science
    • Introduction to Business Process Management
    • Overview of Process Mining approaches
    • Fundamentals of process modeling: Petri Nets, Free-Choice Nets, Workflow Nets, BPMN
    • Structure of event logs
14/2/2019
10:00-13:00
15/2/2019
10:00-13:00
18/2/2019
10:00-13:00
19/2/2019
10:00-13:00
  • Predictive Process Monitoring
    • Predictive Process Monitoring
    • Different types of prediction
    • A framework for predictive process monitoring
    • On-going Directions

Slides


Logs and Models


Links and Additional Material