ML4PM 2023

FOURTH INTERNATIONAL WORKSHOP ON LEVERAGING MACHINE LEARNING IN PROCESS MINING

October 23-27, 2023, Rome (Italy)

http://ml4pm2023.di.unimi.it

AN ACTIVITY FROM THE IEEE TASK FORCE ON PROCESS MINING

About ML4PM

The International Workshop on Leveraging Machine Learning in Process Mining - ML4PM - 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.

ML4PM 2023 will be held in Rome, in conjunction with the ICPM conference.

Call for Papers

This workshop invites papers that present works that lay in the intersection between machine learning and process mining. The event provides a suitable environment to discuss new approaches presented by researchers and practitioners. Main themes include automated process modeling, predictive process mining, application of deep learning techniques and online process mining. The workshop will count with leading researchers, engineers and scientists who are actively working on these topics.

Topics

Topics of interest for submission include, but are not limited to:

• Outcome and time prediction
  
• Classification and clusterization of business processes
• Application of Deep Learning for PM
• Anomaly detection for PM
  
• Natural Language Processing and Text Mining for PM
• Multi-perspective analysis of processes
  
• Machine Learning for robot process automation
  
• Automated process modeling and updating
  
• Conformance checking based on Machine Learning
• Transfer Learning applied to business processes
  
• IoT business services leveraged by Machine Learning
• Multidimensional Process Mining
• Predictive Process Mining
• Prescriptive Learning in Process Mining
• Convergence of Machine Learning and Blockchain in Process Mining

Submission Guidelines

Contributions to all calls should be submitted electronically to the Workshop management system connecting to https://easychair.org/my/conference?conf=icpm2023. At least one author of each accepted paper is expected to participate in the conference and present his/her work.

Submissions must be original contributions that have not been published previously.  Authors are requested to prepare submissions according to the format of the Lecture Notes in Business Information Processing (LNBIP) series by Springer href="http://www.springer.com/computer/lncs?SGWID=0-164-6-791344-0. Submissions must be in English and must not exceed 12 pages (including figures, bibliography and appendices). Each paper should contain a short abstract, clarifying the relation of the paper with the workshop topics, clearly state the problem being addressed, the goal of the work, the results achieved, and the relation to the literature.

A special issue of the Journal of Intelligent Information Systems (Springer, https://www.springer.com/journal/10844) devoted to a selection of the best ICPM workshop papers will be scheduled in the months following the conference.

Registrations are managed by the ICPM system

Important Dates

Keynote Speaker

..................

Jochen De Weerdt

Cracking the Nut: Unraveling Challenges in Predictive Process Monitoring

In this keynote, I will tackle the complexities of Predictive Process Monitoring, focusing on five key challenges. We will delve into strategies for proper model evaluation, discuss generalization in deep learning models, and explore inter-case prediction models. Furthermore, we will make the link from case-level to model-level predictions, showing how process model forecasting can provide answers to tactical process questions. Finally, we will look into the struggle of PPM to improve its adoption in real-life.

Program

Organizers

CHAIRS

• Paolo Ceravolo, Università degli Studi di Milano, Italy (https://orcid.org/0000-0002-4519-0173)
• Sylvio Barbon Jr, Università degli Studi di Trieste, Italy (https://orcid.org/0000-0002-4988-0702)
• Annalisa Appice, Università degli Studi di Bari, Italy (https://orcid.org/0000-0001-9840-844X)

Program Committee

Rafael Accorsi Accenture Switzerland

Annalisa Appice Università degli studi di Bari

Sylvio Barbon Junior University of Trieste

Mario Luca Bernardi University of Sannio

Michelangelo Ceci Universita degli Studi di Bari

Marco Comuzzi Ulsan National Institute of Science and Technology

Carl Corea University of Koblenz-Landau

Jochen De Weerdt KU Leuven

Chiara Di Francescomarino DISI - University of Trento

Marcelo Fantinato University of São Paulo

Maria Teresa Gómez López University of Seville

Sarajane Marques Peres University of São Paulo

Gabriel Marques Tavares Università degli Studi di Milano

Rafael Seidi Oyamada Università degli Studi di Milano

Emerson Paraiso PUCPR - Pontificia Universidade Catolica do Parana

Vincenzo Pasquadibisceglie University of Bari Aldo Moro

Marco Pegoraro RWTH Aachen University

Luigi Pontieri ICAR, National Research Council of Italy (CNR), Italy

Domenico Potena Università Politecnica delle Marche

Flavia Santoro UERJ

Natalia Sidorova Department of Mathematics and Computer Science, Technische Universiteit Eindhoven

Wil van der Aalst RWTH Aachen University

Bruno Zarpelao State University of Londrina