Research-Track Program

The research-track program is composed by one keynote and a number of sessions.

The composition of the research-track sessions follow the keynote description.


Speaker: Prof. Dr. Thomas Seidl, Ludwig-Maximilians-Universität München (LMU Munich), Germany

Title: Data Mining on Process Data

Abstract: Data Mining and Process Mining — is one just a variant of the other, or do worlds separate the two areas from each other? The notions sound so similar but the contents sometimes look differently, so respective researchers may get confused in their mutual perception, be it authors or reviewers. The talk recalls commonalities like model-based supervised and unsupervised learning approaches, and it also sheds light to peculiarities in process data and process mining tasks as seen from a data mining perspective. When considering trace data from event log files as time series, as sequences, or as activity sets, quite different data mining techniques apply and may be extended and improved. A particular example is rare pattern mining, which fills a gap between frequent patterns and outlier detection. The task aims at identifying patterns that occur with low frequency but above single outliers. Structural deficiencies may cause malfunctions or other undesired behavior which get discarded as outliers in event logs, since they are observed infrequently only. Rare pattern mining may identify these situations, and recent approaches include clustering or ordering non-conformant traces. The talk concludes with some remarks on how to sell process mining papers to the data mining community, and vice versa, in order to improve mutual acceptance, and to increase synergies in the fields.

Short Bio: Thomas Seidl is professor for computer science and head of the Database Systems and Data Mining group at LMU Munich. He is co-chair of the nationally funded Munich Center for Machine Learning (MCML), of LMU’s data science lab and of LMU’s master program in data science. Seidl also serves as member of Leibniz Supercomputing Center’s directorate, of many program committees and other scientific boards. His fundamental research on data mining and database technologies with applications in engineering, business, life sciences and humanities yielded more than 300 scientific publications so far. Before returning to Munich in 2016, Seidl held a chair in Computer Science at RWTH Aachen University from 2002, after he had finished his Master, PhD and habilitation in CS in Munich at TUM and LMU, respectively.

Presentation Sessions

Online Operational Support (Tuesday, 6 October, 13.50h-14.30h)

  • Riccardo Galanti, Bernat Coma-Puig, Massimiliano de Leoni, Josep Carmona and Nicolo’ Navarin. Explainable Predictive Process Monitoring.
  • Massimiliano de Leoni, Marcus Dees and Laurens Reulink. Design and Evaluation of a Process-aware Recommender System based on Data-driven Prescriptive Analytics.

Time and Predictions (Tuesday, 6 October, 16.00h-17.00h)

  • Zahra Toosinezhad, Dirk Fahland, Ozge Koroglu and Wil Van der Aalst. Detecting System-Level Behavior Leading To Dynamic Bottlenecks.
  • Eva Klijn and Dirk Fahland. Identifying and Reducing Errors in Remaining Time Prediction due to Inter-Case Dynamics.
  • Tobias Brockhoff, Merih Seran Uysal and Wil M.P. van der Aalst. Time-aware Concept Drift Detection Using the Earth Mover’s Distance.

Data Quality and Preparation (Thursday, 8 October, 12.00h-13.00h)

  • Sareh Sadeghianasl, Arthur ter Hofstede, Suriadi Suriadi and Selen Turkay. Collaborative and Interactive Detection and Repair of Activity Labels in Process Event Logs.
  • Robert Andrews, Fahame Emamjome, Arthur H.M. ter Hofstede and Hajo Reijers. An Expert Lens on Data Quality in Process Mining.
  • Guy Berkenstadt, Avigdor Gal, Arik Senderovich, Roee Shraga and Matthias Weidlich. Queueing Inference for Process Performance Analysis with Missing Life-Cycle Data.

Discovery with Unconventional Input  (Thursday, 8 October, 16.00h-17.00h)

  • Marcus Dees, Bart Hompes and Wil van der Aalst. Events Put into Context (EPiC).
  • Marwa Elleuch, Oumaima Alaoui Ismaili, Nassim Laga, Nour Assy and Walid Gaaloul. Discovery of Activities’ Actor Perspective from Emails based on Speech Acts Detection.
  • Ahmed Awad, Matthias Weidlich and Sherif Sakr. Process Mining over Unordered Streams.

Conformance Checking (Friday, 9 October, 12.00h-13.00h)

  • Manal Laghmouch, Mieke Jans and Benoit Depaîre. Classifying process deviations with weak supervision.
  • Artem Polyvyanyy, Alistair Moffat and Luciano García-Bañuelos. An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining.
  • Mohammadreza Fani Sani, Juan J. Garza Gonzalez, Sebastiaan J. van Zelst and Wil M.P. van der Aalst. Conformance Checking Approximation Using Simulation

Rule Mining (Friday, 9 October, 13.30h-14.30h)

  • Alessio Cecconi, Giuseppe De Giacomo, Claudio Di Ciccio, Fabrizio Maria Maggi and Jan Mendling. Temporal Logic-Based Measurement Framework for Process Mining.
  • Anti Alman, Claudio Di Ciccio, Dominik Haas, Fabrizio Maria Maggi and Alexander Nolte. Rule Mining with RuM.
  • Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa and Artem Polyvyanyy. Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs.

Process Discovery (Friday, 9 October, 14.45h-15.45h)

  • Sander J.J. Leemans, Kanika Goel and Sebastiaan J. van Zelst. Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity.
  • Xixi Lu, Avigdor Gal and Hajo A. Reijers. Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction.
  • Volodymyr Leno, Adriano Augusto, Marlon Dumas, Marcello La Rosa, Fabrizio Maggi and Artem Polyvyanyy. Identifying Candidate Routines for Robotic Process Automation From Unsegmented UI Logs.

Anomaly Detection and Clustering (Friday, 9 October, 16.00h-17.00h)

  • Sylvio Barbon Junior, Paolo Ceravolo, Ernesto Damiani, Nicolas Jashchenko Omori and Gabriel Marques Tavares. Anomaly Detection on Event Logs with a Scarcity of Labels.
  • Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim and Thomas Seidl. TOAD: Trace Ordering for Anomaly Detection.
  • Fareed Zandkarimi, Jana-Rebecca Rehse, Pouya Soudmand and Hartmut Hoehle. A Generic Framework for Trace Clustering in Process Mining.

Every time is intended CEST.