RWTH Process Mining Lecture 17 : Handling Big Event Data, Tooling, Challenges

Lecture 17 of the @RWTH #ProcessMining course presents techniques for streaming and distributed process mining. It is shown that event logs can be decomposed to analyze large event data. Also the topic of comparative process mining is discussed. An overview of tooling is given and the main scientific challenges in process mining are discussed.

Enjoy the course! We hope it will inspire you to dive deeper into this wonderful world of process mining.

Overview
1 Introduction to Process Mining
2 Decision Trees
3 Association Rules & Clustering
4 Introduction to Process Discovery
5 Petri Nets & Alpha Algorithm
6 Alpha Algorithm Continued
7 Quality of Discovered Models and Representations
8 Heuristic Mining
9 Region-Based Mining
10 Inductive Mining
11 Event Data and Exploration
12 Conformance Checking (1/2)
13 Conformance Checking (2/2)
14 Decision Mining
15 Organizational Mining & Bottleneck Analysis
16 Refined Process Mining Framework and Operational Support
17 Dealing with Big Event Data, Tooling, Challenges
Closing

Twitter: #processmining @wvdaalst