The EdbA workshop aims to provide a forum for discussing and developing analytics for various forms of event data. In its most basic form, such data describes discrete events, which occur instantly – and thus have no duration, are ordered in time and are described by a set of attributes. The analytical potential of event data lies in the fact that it captures the dynamic behavior of people, objects and/or systems at a fine-grained level.

 In many real-life settings, the observed behavior and the recorded event data do not follow a strict process with instantaneous, transactional events correlated to a unique instance identifier. Rather, we observe events at various levels of granularity ranging from frequent sensor-based events in IoT settings to recordings of aggregate or long-running behavior involving time intervals and rich information. Behavior often involves multiple entities, objects, and actors to which events can be correlated in various ways ( In these situations, a unique explicit process notion does either not exist, is unclear or different processes or dynamics could be recorded in the same dataset. 
The objective of this workshop is to foster a new research direction within the process mining community on enabling behavioral analytics beyond the classical scope of process mining. At the same time, the workshop aims to invite researchers from other domains and consolidate and integrate research on behavioral event data analytics within the field of process mining.


The Fourth International Workshop on Leveraging Machine Learning in Process Mining is a premier event that aims to foster collaboration and innovation in the intersection of machine learning and process mining. Over the past few years, the combination of these two fields has generated a lot of interest, and this workshop provides an excellent opportunity for researchers and practitioners to share their latest findings and explore new directions for future research.

The workshop will feature a diverse range of papers that showcase the latest advances in automated process modelling, predictive process mining, deep learning techniques, and online process mining. These themes reflect the most exciting and promising areas of research at the intersection of machine learning and process mining. By fostering dialogue and collaboration among participants, the workshop aims to catalyze breakthroughs and push the boundaries of what is possible in this exciting and rapidly-evolving field.


The world’s most valuable resource is no longer oil, but data. The goal of data science techniques is not to collect more data, but to extract knowledge and valuable insights from existing data in various forms. To analyze and improve processes, event data is the main source of information. In recent years, a new discipline has emerged combining traditional process analysis and data-centric analysis: Process-Oriented Data Science (PODS). The interdisciplinary nature of this new research area has resulted in its application to analyze processes in a wide variety of domains. This workshop has an explicit focus on healthcare.

The International Workshop on Process-Oriented Data Science for Healthcare 2023 (PODS4H23) 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 PODS methodologies in healthcare organizations.

This workshop is an initiative of the Process-Oriented Data Science for Healthcare Alliance, which is a chapter within the IEEE Task Force on Process Mining.


Process Mining has proven to be a powerful interdisciplinary tool for addressing open challenges in several fields such as healthcare or finances … 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.

This intersection goes in both directions:

  • Process Mining for Education (PM4Edu): How could process mining be used to address some of the challenges in the field of education? E.g., Process Mining for learning analytics, curricular analytics, motivation trajectories, MOOCs and blended courses, self-regulated learning patterns,…
  • Education for Process Mining (Edu4PM): How could we improve the teaching of the Process Mining discipline? E.g., novel learning strategies tailored for Process Mining, new instruments to automatically assess specific topics of Process Mining, systematic studies of how Process Mining is being taught on different educational programs or levels, a novel curriculum around Process Mining, among others.Add block


The Eighth International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2023) aims to provide a high-quality forum for academics and practitioners to exchange research findings and ideas on methods and practices in the research areas of process querying and process mining, with a focus on techniques, methods, and case studies for process retrieval, evidence-based process improvement, and application of artificial intelligence for process querying and mining.


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.

Key Dates

  • Abstract submission: August 22, 2023
  • Papers submission: August 28, 2023 (extended!)
  • Acceptance notification: September 19, 2023
  • Pre-workshop camera-ready papers: October 3, 2023
  • Workshops: October 23, 2023
  • Post-workshop camera-ready papers: November 7, 2023

Workshop Chairs

Platinum sponsors

Gold sponsors

Silver sponsors

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Sponsors and exhibition