events · November 28, 2023

Second Workshop on Collaboration Mining for Distributed Systems

(COMINDS 2023)


The Second Workshop on Collaboration Mining for Distributed Systems (COMINDS 2023) aims to facilitate the sharing of research findings, ideas, and experiences on new process mining techniques and practices for analyzing collaboration processes. Process mining is a powerful tool for the analysis of business processes carried on by one participant. However, it lacks approaches able to deal with the analysis of collaboration processes implemented by many participants in a distributed system e.g., supply chains involving manufacturers, producers, and retailers; healthcare scenarios involving patients, hospitals, and doctors; or even smart systems like multi-robot and IoT systems. In this setting, confidentiality, privacy, data heterogeneity, and case correlation are only a few of the issues related to data preprocessing. Likewise, there is a lack of discovery algorithms, conformance techniques, and enhancement approaches. Thus, there is a need for approaches to support process mining to fill this gap. In this direction, the workshop point to creating a dialogue centered on the development of scientific foundations enabling the application of process mining in such distributed scenarios.

The main topics relevant to the COMINDS workshop include, but are not limited to:

  • Generation of Synthetic Distributed Event Logs
  • Distributed Event Logs Preprocessing
  • Correlation Mechanisms for Distributed Event Logs
  • Discovery of Collaboration Process Models
  • Conformance Metrics and Techniques for Collaboration Process Models
  • Multi-perspective Analysis of Collaboration Processes
  • Privacy-preserving Process Mining for Distributed Systems
  • Distributed Systems Monitoring and Repair
  • Federated Process Mining
  • Streaming Collaboration Mining


Auditorium Antonianum, room San Bernardino, October 23rd 2023

9:00Prof. Wil M.P. van der AalstUnraveling the Fabric of Intertwined Processes: How Object-Centric Process Mining is changing the way we improve operational processes
Session 1
9:40Giovanni Meroni and Szabolcs GardaMapping Artifact-driven Monitoring Results Back to BPMN Process Diagrams
10:05Julian Rott, Rene Dorsch, Michael Freund, Markus Böhm, Andreas Harth and Helmut KrcmarBreaking Down Barriers with Knowledge Graphs: Data Integration for Cross-Organizational Process Mining
Session 2
11:15Janik-Vasily Benzin and Stefanie Rinderle-MaPetri Net Classes for Collaboration Mining: Assessment and Design Guidelines
11:40Nina Graves, István Koren, Majid Rafiei and Wil M.P. van der AalstFrom Identities to Quantities: Introducing Items and Decoupling Points to Object-centric Process Mining
12:05Leonel Peña, Daniela Andrade, Andrea Delgado and Daniel CalegariAn approach for discovering inter-organizational collaborative business processes in BPMN 2.0
12:30Mahsa Bafrani, Andrea Delgado and Lorenzo RossiClosing


Speaker: Wil van der Aalst

Title: Unraveling the Fabric of Intertwined Processes: How Object-Centric Process Mining is changing the way we improve operational processes

Abstract: Traditional process mining, while powerful and effective, is not without limitations. Data extraction and transformation can be time-consuming and need to be repeated when viewpoints of questions change. Interactions between process objects (e.g., sales orders, sales order items, shipments, invoices, etc.) are not captured in traditional event logs and are, therefore, difficult to analyze using traditional techniques. Object-Centric Process Mining (OCPM) and the newly released OCEL 2.0 standard address these problems. In his keynote, prof. Wil van der Aalst explains the need for OCPM and highlights the changes in going from OCEL 1.0 to OCEL 2.0. He will explain recently developed techniques exploiting object-centricity and briefly relate this to the interplay between process mining and generative and discriminative AI (from classical ML to LLMs).

Short bio: Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University. His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 900 articles and books. According to, he is the highest-ranked computer scientist in Germany and ranked 10th worldwide. According to Google Scholar, he has an H-index of 175 and more than 135.000 citations. Van der Aalst is an IFIP Fellow, IEEE Fellow, ACM Fellow, and received honorary degrees from the Moscow Higher School of Economics (Prof. h.c.), Tsinghua University, and Hasselt University (Dr. h.c.). He is also an elected member of the Royal Netherlands Academy of Arts and Sciences, the Royal Holland Society of Sciences and Humanities, the Academy of Europe, the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts, and the German Academy of Science and Engineering. In 2018, he was awarded an Alexander-von-Humboldt Professorship.


The workshop welcomes submissions of regular papers, short papers, and show&tell presentations.

Regular and Short papers must provide original research contributions significant for the workshop theme that have not been published previously. Submissions must use the Springer LNCS/LNBIP format. Submissions must be in English and must not exceed in total 12 pages for regular papers and 4 pages for short papers. Accepted papers will be published by Springer as a post-workshop proceedings volume in the series Lecture Notes in Business Information Processing (LNBIP). At least one author of each accepted paper must register and participate in the workshop.

Show&Tell presentations could be ongoing research or non-research contributions such as case studies, experiences, lessons learned from projects, industry showcases, and software tool demonstrations significant for the workshop theme. Show&Tell presentations will take place in an interactive and participation-encouraging format based on the number and nature of the accepted contributions. Authors must submit a long abstract of max 2 pages. Show&Tell contributions will be uploaded to the workshop website. At least one author of each accepted Show&Tell must register and participate in the workshop.

The paper should be submitted through the ICPM 2023 submission system selecting the “Collaboration Mining for Distributed Systems” option.

The authors of selected ICPM workshop papers will be invited to submit an extended version of their contribution to the Journal of Intelligent Information Systems (Springer,

Key dates

  • Abstract Submission: August 22, 2023
  • Papers Submission: August 22, 2023 August 28, 2023
  • Acceptance Notification: September 12, 2023 September 19, 2023
  • Pre-workshop Camera-Ready Papers: October 3, 2023
  • Workshops: October 23, 2023
  • Post-workshop Camera-Ready Papers: November 7, 2023

Program Committee

  • Andrea Burattin, Technical University of Denmark, Denmark
  • Chiara Di Francescomarino, Fondazione Bruno Kessler, Italy
  • Cristina Cabanillas, Universidad de Sevilla, Spain
  • Daniel Calegari, Universidad de la República, Uruguay
  • Flavio Corradini, University of Camerino, Italy
  • Adela del Río Ortega, Universidad de Sevilla, Spain
  • Marco Franceschetti, University of St. Gallen, Switzerland
  • Orlenys López Pintado, University of Tartu, Estonia
  • Fabrizio Maria Maggi, Free University of Bozen-Bolzano, Italy
  • Giovanni Meroni, Technical University of Denmark, Denmark
  • Majid Rafiei, RWTH Aachen University, Germany
  • Barbara Re, University of Camerino, Italy
  • Hajo Reijers, Utrecht University, The Netherlands
  • Francesco Tiezzi, University of Florence, Italy
  • Pascal Poizat, University Paris Nanterre, France


Andrea Delgado

Universidad de la República, Uruguay

Mahsa Bafrani

RWTH Aachen University, Germany

Lorenzo Rossi

University of Camerino, Italy

An activity from the IEEE Task Force on Process Mining

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