This 12-part online course introduces you to a new way of understanding and thinking about processes. It shows you how to analyze processes from completely different angles with ready-to-use tools and real-life datasets. This course covers the foundations object-centric process mining and goes beyond object-centric process mining.

The course was created by Dirk Fahland from Eindhoven University of Technology and is aimed at practitioners and researchers who are already experienced in Process Mining and want to get familiar with advanced process mining techniques in their own pace. If you are new to Process Mining, you should first follow the MOOC Process Mining in Action.

Objectives

Processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. Process mining has developed mature methods, models, and solutions for analyzing process event data from the control-flow perspective: the sequence of activities and their relation and impact to performance and process outcomes.

The limitations of control-flow focused analysis are also well-known in the field.

However, it remains challenging to break out of the control-flow oriented models and techniques. In fact, the control-flow oriented techniques have shaped our way of thinking about processes. And as these techniques are limited, so is our thinking and our abilities to identify problems and improve processes. Part 1 of the course shows what these limitations in our thinking are.

This course introduces several recently developed simple models that naturally describe phenomena beyond control-flow, but are inherently compatible with control-flow oriented thinking. The course teaches:

• A fundamentally different way of thinking about processes – driven by the data that is already available and the a set of simple concepts and tools that enable this thinking.
• Models and techniques to study performance patterns, identify local and time-bounded performance problems and their propagation over time, and how to leverage them for process prediction.
• The fundamental limitations of classical control-flow event logs and the basic principlines to overcome them in the paradigm of object-centric process mining.
• Event knowledge graphs as a versatile data structure that enables not only object-centric process mining but also further analysis use cases such as analyzing actor behavior in organizations, identify tasks and organizational routines.
• Techniques to build your own object-centric process mining analysis using graph databases and new ways of identifying weaknesses in processes and root causes of performance problems.
• Ways of modeling and visualizing processes beyond the classical control-flow perspective, for example by modeling control-flow, data object, queue and actor behavior jointly.
• Pointers to various lines of related reseach and trends in this problem space.

For each topic, the course discusses the fundamental problems we face when applying classical control-flow oriented thinking and tools. We discuss which phenomena can be studied when taking a different perspective, which insights can be gained, which tools are available, and to which other fields they relate.

Timeline

We are gradually building up the course material and publish new parts and updates to existing parts. Below is a rough timeline of new parts:

• 20th December 2022: publish complete Part 1 and 2
• until 23rd December 2022: publish basic versions of Parts 3, 4, and 5
• end of January 2023: extended versions of Parts 3, 4, and 5
• February 2023: Parts 6, 7, and 8
• March 2023: Parts 9, 10, 11, and 12

Come back to check for updates and new contents.

Contents

Each part comes with a short video covering a particular topic of processes or analysis techniques that require a “multi-dimensional” way of thinking. Several parts point to concrete datasets and tools with tutorials or step-by-step instructions to immediately try out the techniques as well as self-study exercises and questions to reflect about processes from a new angle.

Enjoy the course!

• Part 1 – Opening: What are processes and what are we missing with current process analysis techniques?
• Part 2 – Performance Spectrum: How to visualize event data in a way that we can analyze multiple process executions at once over time?
• Part 3 – Detecting Emergent Dynamics: How to detect batching, dynamic bottlenecks, and cascades?
• Part 4 – Convergence and Divergence: What kinds of false information is hidden in sequential event logs for processes over multiple data objects?
• Part 5 – Event Knowledge Graphs for Object-Centric Process Mining: How to construct Event Knowledge Graphs to correctly represent event data over multiple objects and how to analyze them?
• Part 6 – Object-Centric Process Analysis with Event Knowledge Graphs: How to infer new information? How to identify causes of delays?
• Part 7 – Beyond Object-Centric: Actor Behavior – How to model and analyze how actors collaborate in processes using event knowledge graphs?
• Part 8 – Event Knowledge Graphs: Tool Support and Examples. What insights can we gain on real-life processes using the concepts of object-centric process mining and multi-dimensional process analysis with the Neo4j graph database system?
• Part 9 – Key Ideas of Multi-Dimensional Process Analysis – What are the fundamental principles of multi-dimensional process analysis?
• Part 10 – Object-Centric Process Discovery
• Part 11 – Process-Queue-Resource (PQR) Systems: A case study on how to model and reason across multiple behavioral dimensions
• Part 12 – Thinking Assistant and Closing: The next step in multi-dimensional process analysis