ERPM

After two successful editions, the Workshop on Empirical Research in Process Mining (ERPM) returns at ICPM 2027. ERPM is the venue dedicated to the empirical methods of our field: how we design studies, collect evidence, and evaluate process mining techniques, tools, and practices.

Process mining has matured into a robust field with a rich landscape of algorithms and tools. Progress is typically assessed through computational metrics such as precision, recall, and fitness. These metrics establish whether a technique is technically accurate. They do not establish whether the technique helps analysts understand their processes, whether organizations can use it effectively, or whether it improves decisions. Answering such questions requires empirical research: controlled experiments, user studies, case studies, field studies, and quantitative analyses of process mining in use.

Workshop topics include, but are not limited to:

• Empirical Evaluation of Process Mining Techniques and Tools: Experiments and user studies assessing the effectiveness, comprehensibility, and usability of process mining artifacts, including behavioral data collection such as eye-tracking, think-aloud protocols, and interaction logs.
• Empirical Methods and Methodology: Research designs, measurement instruments, sampling and recruitment strategies, replication studies, reporting standards, and rigor criteria for empirical process mining research.
• Human-Centric Process Mining Approaches: Empirical studies of the people who conduct and consume process mining — analysts, end-users, and other stakeholders — and their role in the process mining lifecycle, including data preparation, model validation, and results interpretation.

Paper Format, Publication, and Submission Guidelines

We will accept two types of submissions:

• Regular paper submissions in English, up to 12 pages (including tables, figures, bibliography, and appendices), following the Springer LNCS/LNBIP guidelines.



Each paper should contain a short abstract that clarifies the relation of the paper to the workshop topics, clearly stating the problem being addressed, the goal of the work, the results achieved, and the relation to other work. Submissions will be assessed based on significance, originality, technical quality, and potential for relevant discussion.

• Lessons Learned submissions up to 1 page (excluding the bibliography) in the form of an extended abstract, following the same formatting guidelines.



Authors should clearly outline the paper's relevance to the interactive session of the workshop and focus on sharing successes, challenges, and "failed experiments" to provide the audience with a comprehensive overview of best practices and pitfalls in the field of empirical research in process mining.

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Papers must be submitted as self-contained PDF files through the EasyChair system. When submitting, please select the workshop track "3rd Workshop on Empirical Research in Process Mining (ERPM)".

Submissions must be original contributions that have not been published or submitted to other conferences or journals in parallel with this workshop.

Accepted regular papers will be published by Springer in the Lecture Notes in Business Information Processing (LNBIP) series as a post-workshop proceedings volume. Lessons Learned submissions will not be featured in the proceedings.

At least one author of each accepted paper must register and participate in the workshop in person to present the work. Please visit the main conference website for registration information.

Contact erpm-2027 [at] easychair.org if you have any questions.