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.
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 of interest for submission include, but are not limited to:
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
|August 22, 2023
|Notification of Acceptance
|Submission of Camera Ready Papers
|October 3, 2023
|October 23, 2023
|Post-workshop Camera-Ready Papers
|November 7, 2023
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.
|October 23th 2023 - Antonianum, Room A
|Keynote Speech: Cracking the Nut. Unraveling Challenges in Predictive Process Monitoring
|Jochen De Weerdt
|10:30 - 11:15
|Understanding the impact of design choices on the performance of predictive process monitoring
|Sungkyu Kim, Marco Comuzzi and Chiara Di Francescomarino
|Discovering Process-Based Drivers for Case-Level Outcome Explanation
|Peng Li, Hantian Zhang, Xu Chu, Alexander Seeliger and Cong Yu
|Sparse Mixtures of Shallow Linear Experts for Interpretable and Fast Outcome Prediction
|Francesco Folino, Luigi Pontieri, and Pietro Sabatino
|12:45 - 14:15
|Technical Talk: Declarative Process Mining Meets Industry. The Declare4Py Case - GitHub
|Fabrizio Maria Maggi, Ivan Donadello and Francesco Riva
|Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks
|Alessandro Niro and Michael Werner
|15:45 - 16:30
|Uncovering the Hidden Significance of Activities Location in Predictive Process Monitoring
|Mozhgan Vazifehdoostirani, Mohsen Abbaspour Onari, Isel Grau, Laura Genga, and Remco Dijkman
|Extended Abstract: Simulation of unit journeys using process crowding in Generative Adversarial Networks
|Yoann Valero, Frédéric Bertrand and Myriam Maumy
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