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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Process-Driven Architectures

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. The first lecture will take place on Thursday, 20th October, 2016.

 

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. 

 

Exams will take place at the following dates:

  • 20.02.2017
  • 21.02.2017
  • 01.03.2017
  • 02.03.2017
  • 18.04.2017

For each date, there are six slots available (some time between 9:00 and 13:00). Registration for one of these slots needs to happen between 16.01.2017 and 06.02.2017 and is handled by Ms Bah (RUD 25, room 4.402). 

 

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 19, 2017, there will be a guest lecture by Rami Eid-Sabbagh of Lana-Labs on the application of conformance checking in industry projects. (tutorial slides)

 

Dates

VL Do 9-11 RUD 26, Raum 1'303
UE Do 11-13 RUD 26, Raum 1'303

 

 

 

 

Lecture Slide

 

Recitations 

 

Presentation Slots

Date Topic and Presenters
   

December 8, 2016

3) Genetic Mining
Dominik Kloke und Jan Dangel

 

4) Discovery based on Regions
Emma Hennig and Lukas Abegg

 

8) Discovery of Roles
Belhassen Ouerghi and Tugce Aksel
   

December 15, 2016

7) Artifact-based Discovery
Thuy-Vi Vo and Marina Serpinskaya

 

9) Stream-based Discovery
Michael Aringer and Matthias Menzel

 

 

February 2, 2017

6) Handling Duplicated Tasks
Matthias Becher and Markus Richter

 

13) Alternative Measures for Model Precision
Vanessa Chanliau and Dmytro Fradin

 

16) Temporal Anomaly Detection
Nargiz Bakhshaliyeva and David Rodriguez Edel

 

 

February 9, 2017

17) Queue Mining
Stephan Fahrenkrog-Petersen and Hermann Stolte

 

18) Predictive Monitoring
Valentin Zambelli and Simon Remy

 

19) Sequence Encodings in Predictive Monitoring
Carl Tramburg and Kamarhulrhizwan Benjamin Jaidi

 

Presentation Topics 

Note on the dates: topics 1-9 are likely to be scheduled for December 8 or December 15, 2016. The remaining topics will be presented in February 2017.

 
1) Event Abstractions
Thomas Baier, Jan Mendling, Mathias Weske: Bridging abstraction layers in process mining. Inf. Syst. (IS) 46:123-139 (2014) paper link
 
2) 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
 
3) 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
 
4) Discovery based on Regions
Josep Carmona, Jordi Cortadella, Michael Kishinevsky: A Region-Based Algorithm for Discovering Petri Nets from Event Logs. BPM 2008: 358-373 paper link
 
5) 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
 
6) 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
 
7) Artifact-based Discovery
Xixi Lu, Marijn Nagelkerke, Dennis van de Wiel, Dirk Fahland: Discovering Interacting Artifacts from ERP Systems. IEEE Trans. Services Computing 8(6): 861-873 (2015) paper link
 
8) Discovery of Roles
Andrea Burattin, Alessandro Sperduti, Marco Veluscek: Business models enhancement through discovery of roles. CIDM 2013:103-110 paper link
 
9) 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
 
10) Event Structures for Process Mining
Marlon Dumas, Luciano García-Bañuelos: Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs. Petri Nets 2015: 33-48 paper link
 
11) Declarative Model Discovery
Fabrizio Maria Maggi, R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst: Efficient Discovery of Understandable Declarative Process Models from Event Logs. CAiSE 2012: 270-285 paper link
 
12) Consistency in Declarative Model Discovery
Claudio Di Ciccio, Fabrizio Maria Maggi, Marco Montali, Jan Mendling: Ensuring Model Consistency in Declarative Process Discovery. BPM 2015: 144-159 paper link
 
13) Alternative Measures for Model Precision
Jorge Munoz-Gama, Josep Carmona: A Fresh Look at Precision in Process Conformance. BPM 2010:211-226 paper link
 
14) 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
 
15) Anti-Alignments
Boudewijn F. van Dongen, Josep Carmona, Thomas Chatain: A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments. BPM 2016: 39-56 paper link
 
16) Temporal Anomaly Detection
Andreas Rogge-Solti, Gjergji Kasneci: Temporal Anomaly Detection in Business Processes. BPM 2014:234-249 paper link
 
17) Queue Mining
Arik Senderovich, Matthias Weidlich, Avigdor Gal, Avishai Mandelbaum: Queue mining for delay prediction in multi-class service processes. Inf. Syst. (IS) 53:278-295 (2015) paper link
 
18) Predictive Monitoring
Fabrizio Maria Maggi, Chiara Di Francescomarino, Marlon Dumas, Chiara Ghidini: Predictive Monitoring of Business Processes. CAiSE 2014:457-472 paper link
 
19) Sequence Encodings in Predictive Monitoring
Anna Leontjeva, Raffaele Conforti, Chiara Di Francescomarino, Marlon Dumas, Fabrizio Maria Maggi: Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes. BPM 2015: 297-313 paper link
 
 

See AGNES for further details: