Process Mining (VL/UE)
Prof. Dr. Matthias Weidlich
Dr. Han van der Aa
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
The course features lectures and recitations that focus on the formal foundations and basic techniques of process mining. Specifically, this includes algorithms for process discovery that construct models from event data. Also, essential conformance checking techniques to identify deviations between models and event data, e.g., by replay or alignment construction will be discussed. Finally, advanced techniques for model extension, process simulation, and performance prediction will be reviewed. As part of excercises, course participants will be exposed to real-world data and prototype process mining techniques.
The lectures and recitations are complemented by seminar-style presentations on state-of-the-art developments in the field. Each participant 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.
The first lecture will take place on Monday, 22nd October, 2018.
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.
The course can only be taken by students that have not completed the old module Q5-5 of the same name.
Credit Points
The course counts for 9 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".
Dates
| VL | Mo 9-11 | RUD 26, Raum 1'307 |
| UE | Mo 11-13 | RUD 26, Raum 1'307 |
| VL | Tu 9-11 | RUD 26, Raum 1'307 |
Lecture Slides
- Lecture 1: Overview
- Lecture 2: Event logs
- Lecture 3: Event Mapping
- Lecture 4: Alpha
- Lecture 5: Alpha variants (updated)
- Lecture 6: Heuristic
- Lecture 7: Fuzzy
- Lecture 8: Inductive
- Lecture 9: Evaluation
- Lecture 10: Conformance
- Lecture 11: Alignments (updated)
- Lecture 12: Alignments Computation and Measures
- Lecture 13: Multiple Trace Alignments
- Guest lecture on Process Intelligence at Signavio
- Lecture 14: General View on Conformance (updated)
- Lecture 15: Enhancement: Simulation
- Lecture 16: Enhancement: Time Prediction
- Lecture 17: Enhancement: Queue Mining
- Lecture 18: Enhancement: Predictive Monitoring
- Lecture 19: Final Comments
Recitations
- Exercise 1: Formalisms
- Exercise 2: Log Exploration (updated) (code for exercise 2 (updated)) (example solution)
- Exercise 3: Case Construction (code for exercise 3)
- Exercise 4: Alpha (code for exercise 4) (example solution)
- Exercise 5: Fuzzy and ProM
- Exercise 6: Inductive
- Exercise 7: Measures
- Exercise 8: Rel Conformance (code for exercise 8) (example solution)
- Exercise 9: Alignments
- Exercise 10: Alignments cont.
- Exercise 11: Multiple Perspectives
- Exercise 12: Prediction
- Exercise 13: Predictive Monitoring (code for exercise 13)
Presentation Slots
| Date | Topic and Presenters |
|---|---|
|
November 26, 2018 |
1) Event Ordering Imperfection |
|
|
3) Interaction Mining |
|
December 18, 2018 |
7) Stream-based Discovery |
|
|
8) Discovery of BPMN Models |
| January 7, 2019 |
5) Genetic Mining |
|
January 28, 2019 |
11) Concept Drift |
|
12) Time Drifts |
|
|
14) Decomposing Conformance Checking |
|
|
February 4, 2019 |
16) Discovery of Roles |
|
|
17) Temporal Anomaly Detection |
Presentation Topics
Note on the dates: topics 1-10 are likely to be scheduled for end of November or December 2018. The remaining topics will be presented in Jan and Feb 2019.
1) Event Ordering Imperfection
Prabhakar M. Dixit, Suriadi Suriadi, Robert Andrews, Moe Thandar Wynn, Arthur H. M. ter Hofstede, Joos C. A. M. Buijs, Wil M. P. van der Aalst: Detection and Interactive Repair of Event Ordering Imperfection in Process Logs. CAiSE 2018: 274-29 paper link
2) Pattern-based Abstractions
Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers, Wil M. P. van der Aalst, Pieter J. Toussaint: From Low-Level Events to Activities - A Pattern-Based Approach. BPM 2016: 125-141 paper link
3) Interaction Mining
Arik Senderovich, Andreas Rogge-Solti, Avigdor Gal, Jan Mendling, Avishai Mandelbaum: The ROAD from Sensor Data to Process Instances via Interaction Mining. CAiSE 2016: 257-273 paper link
4) Overlapping Log Entries
Ahmed Awad, Nesma M. Zaki, Chiara Di Francescomarino: Analyzing and repairing overlapping work items in process logs. Information & Software Technology 80: 110-123 (2016) paper link
5) Genetic Mining
Wil M. P. van der Aalst, Ana Karla A. de Medeiros, A. J. M. M. Weijters: Genetic Process Mining. ICATPN 2005:48-69 paper link
6) Discovery based on Regions
Josep Carmona, Jordi Cortadella, Michael Kishinevsky, Alex Kondratyev, Luciano Lavagno, Alexandre Yakovlev: A Symbolic Algorithm for the Synthesis of Bounded Petri Nets. Petri Nets 2008: 92-111 paper link
7) Stream-based Discovery
Andrea Burattin, Alessandro Sperduti, Wil M. P. van der Aalst: Control-flow discovery from event streams. IEEE Congress on Evolutionary Computation 2014: 2420-2427 paper link
8) Discovery of BPMN Models
Raffaele Conforti, Marlon Dumas, Luciano García-Bañuelos, Marcello La Rosa: Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers. BPM 2014:101-117 paper link
9) Discovery of Process Model Patterns
Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst, Sylvie Norre: Interest-driven discovery of local process models. Inf. Syst. 77: 105-117 (2018) paper link
10) Handling Duplicated Tasks
Xixi Lu, Dirk Fahland, Frank J. H. M. van den Biggelaar, Wil M. P. van der Aalst: Handling Duplicated Tasks in Process Discovery by Refining Event Labels. BPM 2016: 90-107 paper link
11) Concept Drift
Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, Alireza Ostovar: Detecting Sudden and Gradual Drifts in Business Processes from Execution Traces. IEEE Trans. Knowl. Data Eng. 29(10): 2140-2154 (2017) paper link
12) Time Drifts
Florian Richter, Thomas Seidl: TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. 289-305 paper link
13) Negative Events in Model Evaluation
Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens: Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Trans. Knowl. Data Eng. 26(8): 1877-1889 (2014) paper link
14) Decomposing Conformance Checking
Jorge Munoz-Gama, Josep Carmona, Wil M. P. van der Aalst: Single-Entry Single-Exit decomposed conformance checking. Inf. Syst. 46: 102-122 (2014) paper link
15) Approximating Alignments
Boudewijn F. van Dongen, Josep Carmona, Thomas Chatain, Farbod Taymouri: Aligning Modeled and Observed Behavior: A Compromise Between Computation Complexity and Quality. CAiSE 2017: 94-109 paper link
16) Discovery of Roles
Andrea Burattin, Alessandro Sperduti, Marco Veluscek: Business models enhancement through discovery of roles. CIDM 2013:103-110 paper link
17) Temporal Anomaly Detection
Andreas Rogge-Solti, Gjergji Kasneci: Temporal Anomaly Detection in Business Processes. BPM 2014:234-249 paper link
18) Feature Engineering
Arik Senderovich, Chiara Di Francescomarino, Chiara Ghidini, Kerwin Jorbina, Fabrizio Maria Maggi: Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions. 306-323 paper link
See AGNES for further details: