Workshops

This is the list of the workshop co-located with ICPM 2027.

Table of Contents

Here are the key dates (Workshop Papers):

Author Abstract Submission: November 11, 2026
Author Paper Submission: November 18, 2026
Author Paper Acceptance Notification: December 21, 2026
Author Pre-workshop Camera-Ready Papers: January 21, 2027
Workshops: February 8, 2027
Post-workshop Camera-Ready Papers: February 22, 2027

BPI – Business Process Intelligence

Website: https://bpi-workshop.github.io/

Business Process Intelligence (BPI) refers to the application of data- and process-mining techniques within Business Process Management. The field spans process discovery, conformance checking, process analytics, process modeling, optimization, monitoring, and many other approaches that are increasingly important in both academia and industry. The BPI workshop has a long-standing tradition as a venue for innovative research on quantitative process analysis. It provides an opportunity to discuss ongoing research, share practical experiences, exchange ideas, and identify future research directions. We particularly welcome work on traditional process mining, process discovery, conformance checking, formal methods, and descriptive process intelligence approaches. In 2027, we will have the BPI challenge return by using a real-life object-centric dataset.

This will offer the participants to once again demonstrate the capabilities of existing process mining tools, provide a context for future research in a similar fashion to the previous challenges by offering a shareable body of work and one of the first object-centric real-life event logs.

Organized by:

Andrea Burattin, Technical University of Denmark
Johannes De Smed, KU Leuven, Belgium
Marwan Hassani, Eindhoven University of Technology, Netherlands

CoMinDS – Collaboration Mining for Distributed Systems

Website: https://cominds-ws.github.io/

The Fifth Workshop on Collaboration Mining for Distributed Systems (CoMinDS) promotes the exchange of research findings, ideas, and practical experiences on process mining techniques for analyzing collaborative processes. While process mining effectively examines business processes performed by a single participant, existing approaches remain limited when processes involve multiple participants within distributed systems. Such collaboration is common in supply chains involving manufacturers, suppliers, and retailers; healthcare settings connecting patients, hospitals, and physicians; and smart environments such as multi-robot and IoT systems. In these contexts, processes are interconnected, while sharing event data may be difficult because of privacy, security, or organizational constraints. Object-centric process mining further supports the analysis of interactions and dependencies among multiple objects and organizations. This is especially relevant when data is distributed across domains with different formats, structures, and privacy-driven requirements. The workshop underscores the need to develop techniques that facilitate comprehensive process analysis among different parties.

Organized by

Andrea Delgado, Universidad de la República, Uruguay
Sara Pettinari, Gran Sasso Science Institute, Italy
Mahsa Pourbafrani, RWTH Aachen University, Germany
Mathias Weske, Hasso-Plattner-Institut, University of Potsdam, Germany

EdbA – Event Data and Behavioral Analytics (TBD)

EduPM – Education meets Process Mining

Website: https://edupm.org

Process Mining has proven to be a powerful interdisciplinary tool for addressing open challenges in several fields such as healthcare or finance… and Education is no exception. The recent Process Mining approaches proposed for learning analytics, curricular analytics, or MOOC analytics are just some examples. But the Education discipline is also contributing to Process Mining, providing best practices, lessons learned, and new artifacts for better teaching and assessing Process Mining. The International Workshop on Education meets Process Mining (EduPM) aims at providing a high-quality forum for the intersection of Education and Process Mining.

Organized by

Jorge Munoz-Gama, Pontificia Universidad Católica de Chile
Wil van der Aalst, RWTH Aachen University, Germany
Laura Genga, Eindhoven University of Technology, The Netherlands
Michael Arias, Universidad de Costa Rica, Costa Rica

ERPM – Empirical Research in Process Mining

Website: https://erpm-workshop.github.io/homepage/

The 3rd Workshop on Empirical Research in Process Mining (ERPM) is dedicated to the empirical methods of the process mining field: how we design studies, collect evidence, and evaluate process mining techniques, tools, and practices. Technical progress in process mining is typically assessed through computational metrics such as precision and recall. These metrics establish whether a technique is accurate, but not whether it helps analysts understand their processes, whether organizations can use it effectively, or whether it improves decisions. ERPM provides a platform for research that answers such questions through controlled experiments, user studies, case studies, field studies, and quantitative analyses of process mining in use. Beyond regular papers, the workshop features a dedicated Lessons Learned session in which researchers share experiences, challenges, and failed experiments, as well as a keynote lecture. ERPM welcomes full papers, study designs with first pretest results, and theoretical analyses toward testable hypotheses.

Organized by

Djordje Djurica, Vienna University of Economics and Business, Austria
Amine Abbad-Andaloussi, University of St. Gallen, Switzerland
Kateryna Kubrak, Celonis, Germany

GenAI4PM – Generative AI for Process Mining

Website: https://www.genai4pm2027.info/

GenAI4PM 2027 brings together researchers, practitioners, and tool builders interested in the intersection of generative AI, foundation models, and process mining. The workshop welcomes original research, empirical studies, tools, datasets, case studies, and experience reports on topics such as event data extraction and enrichment, conversational process analytics, process model generation, process discovery, conformance checking, root cause analysis, object-centric process mining, multimodal approaches, autonomous agents, and integration with enterprise systems. Particular emphasis is placed on rigorous evaluation, shared benchmarks, reproducibility, and trustworthy AI, including reliability, explainability, robustness, fairness, privacy, hallucination mitigation, and verification of generated artifacts. GenAI4PM 2027 aims to connect academic and industrial perspectives, identify open challenges, and shape a practical research agenda for reliable and effective generative AI in process intelligence.

Organized by

Alessandro Berti, RWTH Aachen University, Germany
Mohammadreza Fani Sani, Microsoft, Denmark
Humam Kourani, Fraunhofer Institute for Applied Information Technology (FIT), Germany
Cristina Cabanillas, University of Seville, Spain

ML4PM – Leveraging Machine Learning in Process Mining

Website: https://ml4pm.di.unimi.it/

The Seventh International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) is a premier academic event dedicated to exploring the dynamic intersection of machine learning and process mining. The workshop aims to foster collaboration and innovation among researchers, engineers, and practitioners by providing a platform to share cutting-edge findings and shape future research directions.

Contributions on a wide array of specialised topics are invited, including predictive process monitoring, anomaly detection, natural language processing, large language models, and prescriptive learning. The ML4PM final goal is to catalyze breakthroughs, push the boundaries of current methodologies, and address complex challenges in business process management through advanced, data-driven machine learning techniques.

Organized by

Paolo Ceravolo, Università degli Studi di Milano, Italy
Sylvio Barbon Junior, Università degli Studi di Trieste, Italy
Vincenzo Pasquadibisceglie, Università degli Studi di Bari, Italy

PLC – Processes, Laws, and Compliance

Website: https://plc.di.unito.it/plc-workshop-2027/

The Processes, Laws, and Compliance (PLC) workshop provides a forum for exchanging research findings and ideas on data-driven and process-oriented techniques in the legal domain, fostering collaboration among researchers and practitioners working across IT and law. Legal informatics has grown alongside the spread of information systems that record legal processes, legal event logs, tender documents, and the temporal dimensions of legal procedures. Artificial Intelligence and data-driven techniques can support the analysis of these data, but do not alone provide full transparency on how processes and law intersect. Process discovery, conformance checking, and process repair can help address this intersection. Relevant applications include the discovery of legal procedures from historical data, the formalisation of legal requirements, regulatory compliance checking, and the prediction of ongoing legal cases through Process Mining, Machine Learning, and Natural Language Processing. The workshop also addresses challenges related to deployment, privacy-sensitive analysis, and potential biases in predictive outcomes.

Organized by

Laura Genga, Technical University of Eindhoven, Netherlands
Hugo A. López, Technical University of Denmark, Denmark
Emilio Sulis, University of Turin, Italy
Roberto Nai, University of Turin, Italy

PM4S – Process Mining for Sustainability

Website: https://pm4s-ws.github.io/workshop/

The PM4S workshop aims to provide a platform for researchers and practitioners to explore the intersection of process mining and sustainability and raise awareness for the potential of process mining for supporting sustainable development. We want to offer a platform

to present work contributing to the topic and foster discussion and collaboration on innovative approaches to enhance the environmental and social performance of business processes.

Organized by

Nina Graves, RWTH Aachen University, Germany
Andreas Fritsch, Karlsruhe Institute of Technology, Germany
Martin Rubio, Universidad de la República, Uruguay
István Koren, ELTE University Budapest, Hungary & RWTH Aachen University, Germany

PODS4H – Process-Oriented Data Science for Healthcare

Website: https://www.pods4h.com/pods4h-workshop/2027

The 9th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H27) provides a high-quality forum for interdisciplinary researchers and practitioners to exchange research findings and ideas on data-driven process analysis techniques and practices in healthcare. PODS4H research includes a variety of topics ranging from process mining techniques adapted for healthcare processes to practical issues related to the implementation of PODS4H methodologies in healthcare organizations. During the workshop, we aim to bring together researchers and practitioners in a spirit of collaboration and co-creation. In this way, we aim to advance PODS4H research and practice, taking into account the distinctive characteristics and challenges of the healthcare domain.

Organized by

Niels Martin, Hasselt University, Belgium
Carlos Fernandez-Llatas, Universitat Politècnica de València, Spain
Owen Johnson, University of Leeds, UK
Marcos Sepúlveda, Pontificia Universidad Católica de Chile
Jorge Munoz-Gama, Pontificia Universidad Católica de Chile

PROMISE – Process Mining for Intelligent, Sustainable, and Explainable AI-Driven Systems

Website: https://sites.google.com/view/promise-2027/home

PROMISE 2027: Process Mining for Intelligent, Sustainable, and Explainable AI-Driven Systems is a f workshop that brings together researchers, practitioners, and industry experts to explore the next generation of process mining empowered by artificial intelligence. As organizations increasingly rely on data-driven decision-making, process mining is evolving beyond traditional process analysis through the integration of emerging technologies such as Large Language Models (LLMs), Agentic AI, Federated Learning, Digital Twins, and Sustainable AI. The workshop provides a forum for presenting novel research, industrial applications, and practical tools that address challenges related to explainability, privacy, scalability, security, and sustainability in process-aware systems. Through keynote talks, peer-reviewed paper presentations, and interactive discussions, PROMISE 2027 aims to foster interdisciplinary collaboration and identify future research directions at the intersection of Process Mining, Artificial Intelligence, Business Process Management, and Process Intelligence, enabling the development of intelligent, responsible, and sustainable digital transformation solutions.

Organized by

Dipanwita Thakur, University of Calabria, Italy
Daniela Annunziata, University of Naples Federico II, Italy
Antonella Guzzo, University of Calabria, Italy
Francesco Piccialli, Optym, Australia

R2PM – Robust and Resilient Process Mining

Website: https://r2pm-workshop.github.io/

Event data is often imperfect, incomplete, or biased. Process mining algorithms rely on hidden assumptions, and are affected by unknown errors, or misaligned heuristics. Issues stemming from individual steps in the process mining pipeline ripple through the analysis, inhibiting reliable and valid outcomes., Experimental research in process mining frequently relies on a narrow selection of event logs or imperfect metrics, resulting in findings that lack generalisability and robustness across diverse, real-world processes. In response to these issues, the R2PM-workshop disseminates and discusses robust and resilient process mining. Here, robustness refers to the ability of systems, algorithms, or techniques to prevent issues stemming from faulty, erroneous data, or uninformed use, while resilience refers to the ability of such systems, algorithms, or techniques to react to or recover from such factors. Topics of interests include data quality management, algorithmic reliability, analytical validity, data governance, effective result visualization, or the effective and reliable use of process mining techniques.

Organized by

Yannis Bertrand, Hasselt University, Belgium
Martin Kabierski, Humboldt-Universitat zu Berlin, Germany
Jari Peeperkorn, KU Leuven, Belgium
Gyunam Park, RWTH Aachen University, Germany
Shazia Sadiq, University of Queensland, Australia

SMA4PM – Stream Management & Analytics for Process Mining

Website: https://sma4pm.github.io/2027/

The SMA4PM 2027 workshop, held with ICPM, focuses on the emerging field of Streaming Process Mining. It explores online analytics for business processes under time and storage constraints, bridging process mining with stream data mining, time series, and evolving graphs. Key topics include process discovery, conformance checking, predictive analytics, concept drift detection, and online outlier detection. The workshop also addresses event management, including correlation, rule-based consumption, and decentralized execution for process orchestration. Building on five successful editions, it unites researchers and practitioners from Process Mining, BPM, and Database Systems to exchange ideas, share industrial experiences, and define future research directions for real-time process analysis.

Organized by

Marwan Hassani, Eindhoven University of Technology, The Netherlands
Thomas Seidl, Ludwig-Maximilians-Universität München, Germany
Andrea Burattin, Technical University of Denmark
Ahmed Awad, The British University in Dubai, Emirates
Gabriel Marques Tavares, Ludwig-Maximilians-Universität München, Germany

SUN-PM – Stochastics, Uncertainty and Non-Determinism in Process Mining

Website: http://sun-pm.com/

Traditional process mining methods typically assume deterministic and noise-free event data, yet real-world scenarios often involve stochastic and probabilistic effects, uncertain information, incomplete traces, loss of order, and partial observability of events. As a consequence, there is a growing need for analysis techniques that accommodate stochastics, uncertainty, and non-determinism in process mining. When associated with events, attributes, traces, or the components of a model, this type of information or meta-information has been shown to be useful to the goal of obtaining more accurate and trustworthy results from process-centric analyses. The International Workshop on Stochastics, Uncertainty, and Non-determinism in Process Mining (SUN-PM) aims to provide reach and visibility, collect, and disseminate research ideas that apply process mining in contexts where models and/or data contain or represent probabilistic, non-deterministic, partially-ordered, or fuzzy behavior.

Organized by

Marco Pegoraro, RWTH Aachen University, Germany
Xixi Lu, Utrecht University, The Netherlands
Paolo Ballarini, Université Paris Saclay, France
Adam Burke, Queensland University of Technology, Australia
Natalia Sidorova, Eindhoven University of Technology, The Netherlands