Process Mining (VL/UE)
Prof. Dr. Matthias Weidlich
Content
One emerging branch of data science is process mining. In the field of process automation, process mining aims at deriving qualitative and quantitative insights on the execution of a process based on recorded events logs.
Structure
In the first part of the course, lectures and recitations will focus on the formal foundations and basic techniques of process mining. That includes algorithms for process discovery (constructing models from event data), conformance checking (identifying deviations between models and event data), and model extension (enriching models based on event data). The recitations will include a tutorial in which the students are exposed to real-world data and process mining tools.
The second part of the course will be organised as a seminar. Each student will be asked to read a recent research paper on process mining (selection from a given list) and give a critical assessment of the approach presented in the paper in the form of a 45min presentation.
The course will be given in English unless all students unanimously vote for German as the teaching language in the first lecture. The first lecture will take place on Thursday, 16th of April, 2015.
Exam
There will be an oral exam at the end of the semester. To be eligible to take the exam, each student will be required to give a presentation (45min) on a research paper in the second part of the course.
Credit Points
The course counts for 5 LP and is open for: Informatik, Master of Science (M.Sc.) Informatik, Master of Education (M.Ed.) Wirtschaftsinformatik, Master of Science (M.Sc.). The related area of specialisation is "Daten- und Wissensmanagement".
Guest Lecture
On January 12, 2016, there will be a guest lecture by Danilo Schmiedel of Opitz Consulting on the use of process mining techniques in industry projects, with a focus on case handling approaches. Slides of the guest lecture.
Dates
VL | Di 9-11 | RUD 26, Raum 1'307 |
UE | Di 11-13 | RUD 26, Raum 1'307 |
Lecture Slides
- Lecture 1 - Context
- Lecture 2 - Logs
- Lecture 3 - Discovery: Alpha-Algorithms
- Lecture 4 - Discovery: Heuristic and Fuzzy Miners
- Lecture 5 - Discovery: Inductive Miner
- Lecture 6 - Evaluation Measures
- Lecture 7 - Conformance Checking: Replay and Relational Approaches
- Lecture 8 - Conformance Checking: Alignments
- Lecture 9 - Enhancement
- Tutorial Handout
Recitations
- Assignment Sheet 1: Behavioural Formalisms
- Assignment Sheet 2: Logs
- Assignment Sheet 3: Alpha-Algorithms
- Assignment Sheet 4: Heuristic Miner
- Assignment Sheet 5: Fuzzy Miner, Process Trees
- Assignment Sheet 6: Inductive Miner, Measures
- Assignment Sheet 7: Conformance Checking
- Assignment Sheet 8: Alignment
- Assignment Sheet 9: Enhancement (solution sketch)
- Tutorial example file
Presentation Topics
See AGNES for further details:
- M.Sc. 3313075 (VL) and 3313076 (UE)