Call for Research Papers

The International Conference on Process Mining (ICPM) is the premium forum for researchers, practitioners and developers in process mining. The objective is to explore and exchange knowledge in this field through scientific talks, industry discussions, contests, technical tutorials and panels. The conference covers all aspects of process mining research and practice, including theory, algorithmic challenges, applications and connections with other fields.

The conference is technically co-sponsored by the IEEE Computational Intelligence Society, and supported by the IEEE Task Force on Process Mining.

Process mining is an innovative research field which focuses on extracting business process insights from transactional data commonly recorded by IT systems, with the ultimate goal of analyzing and improving organizational productivity along performance dimensions such as efficiency, quality, compliance and risk. By relying on data rather than perceptions gained from interviews and workshops, process mining shifts the way of thinking from “confidence-based” to “evidence-based” business process management. Thus, process mining distinguishes itself within the information systems domain by its fundamental focus on understanding, analyzing, and improving business processes based on process data.

Current process mining challenges include scalability, i.e., dealing with volume, velocity and variability of input data, especially in real-time/online settings using event streams; approximation, i.e., balancing computation time with accuracy; understandability and explainability, i.e., providing easy-to-understand and explainable analytics; multi-perspective analysis, i.e., considering data, resources and time beyond the process control flow; measurability, e.g., providing a comprehensive framework for measuring differences between observed and modelled process behavior, and ethical and confidential aspects of process mining, i.e., how to ensure that process mining procedures and results do not violate ethical and privacy principles.

ICPM 2021 will take place on October 31st-November 4th at the Campus of the Eindhoven University of Technology, The Netherlands.

TOPICS FOR RESEARCH PAPERS

ICPM 2021 encourages papers on new methodologies, techniques and applications for process mining, as well as case studies coming from industrial scenarios. Also, papers describing novel tools, fundamental research and empirical studies on process mining are expected. For the sake of replicability of the presented studies, the addition of supplementary resources is strongly encouraged, such as: used datasets, a publicly accessible implementation for new techniques, experimental packages for empirical studies. The use of novel, previously unpublished datasets is most welcome. Research on existing datasets must clearly showcase the novelty or unprecedented results of the applied analysis.

Selected, accepted research papers will be considered for publication in an extended and revised form in a special issue of an international journal.

The thematic areas in which contributions are sought include, but are not limited to, those listed below.

Process mining techniques:

  • Automated Discovery of Process Models
  • Conformance/compliance Analysis
  • Construction of Event Logs
  • Event Log Quality Improvement
  • Decision Mining for Processes
  • Rule/constraint-based Process Mining
  • Mining from non-process-aware systems / event streams
  • Multi-perspective Process Mining
  • Simulation/optimization for Process Mining
  • Predictive Process Analytics
  • Prescriptive Process Analytics and Recommender Systems
  • Responsible Process Mining
  • Privacy-preserving Process Mining
  • Process Model Repair
  • Process Performance Mining
  • Variants/deviance Analysis and Root-cause Analysis
  • Visual Process Analytics
  • Process Monitoring

Process mining fundamental research:

  • Formal Foundations of Process Mining
  • Comparative and Benchmark Studies on Process Mining
  • Conceptual Models Related to Process Mining
  • Human-centered Studies on Process Mining
  • Process Mining Quality Measures
  • Process Mining Guidelines

Process mining applications and case studies in:

  • Artificial Intelligence
  • Blockchain Technologies
  • Robotic Process Automation (RPA)
  • Business Activity Monitoring and Business Intelligence
  • (Cyber) Security and Privacy
  • Operations Management and Lean Six Sigma
  • Process Performance Measurement
  • Process Reengineering
  • Resource Management
  • Risk Management
  • Sensors, Internet-of-Things (IoT) and Wearable Devices
  • Specific domains (such as accounting, finance, government, healthcare, manufacturing)

SUBMISSION INSTRUCTIONS

Submissions must be original contributions that have not been published previously, nor submitted elsewhere while being submitted to ICPM 2021.  All files must be prepared using the latest IEEE Computational Intelligence Society conference proceedings guidelines (8.5″ × 11″ two-column format).

The page limit is set to 8 pages (IEEE Format). All papers must be in English. Templates are available for Latex and Word here.

The paper should be submitted through the ICPM 2021 submission system selecting the  “Research Track” option.

At least one author of each accepted contribution is expected to register for the conference and present the paper, along with signing a copyright release form.

Key Dates

  • Abstract Submissions:  15 June, 2021
  • Full-Paper Submissions: 1 July, 2021
  • Notification: 15 August 2021
  • Camera-ready Submissions: 15 September 2021
  • ICPM conference: 2-4 November 2021

PROGRAM COMMITTEE (tentative)

  • Robert Andrews (Queensland University of Technology, Australia)
  • Abel Armas Cervantes (The University of Melbourne, Australia)
  • Ahmed Awad (University of Tartu, Estonia)
  • Hyerim Bae (Pusan National University, South Korea)
  • Amin Beheshti (Macquarie University, Australia)
  • Robin Bergenthum (FernUniversität in Hagen, Germany)
  • Andrea Burattin (Technical University of Denmark, Denmark)
  • Cristina Cabanillas (Vienna University of Economics and Business, Austria)
  • Josep Carmona (Universitat Politècnica de Catalunya, Spain)
  • Paolo Ceravolo (Università degli Studi di Milano, Italy)
  • Thomas Chatain (LSV, ENS Paris-Saclay, Cachan, France)
  • Marco Comuzzi (Ulsan National Institute of Science and Technology, South Korea)
  • Benjamin Dalmas (Mines Saint-Etienne, France)
  • Massimiliano de Leoni (University of Padua, Italy)
  • Johannes De Smedt (The University of Edinburgh, UK)
  • Jochen De Weerdt (Katholieke Universiteit Leuven, Belgium)
  • Søren Debois (IT University of Copenhagen, Denmark)
  • Pavlos Delias (Eastern Macedonia and Thrace Institute of Technology, Macedonia)
  • Benoît Depaire (Hasselt University, Belgium)
  • Remco Dijkman (Eindhoven University of Technology, the Netherlands)
  • Marlon Dumas (University of Tartu, Estonia)
  • Joerg Evermann (Memorial University of Newfoundland, Canada)
  • Dirk Fahland (Eindhoven University of Technology, the Netherlands)
  • Carlos Fernández-Llatas (Universitat Politècnica de València, Spain)
  • Francesco Folino (ICAR-CNR, Italy)
  • Avigdor Gal (Technion, Israel)
  • Walid Gaaloul (Computer Science Department Télécom SudParis, France)
  • Luciano García-Bañuelos (Tecnológico de Monterrey, Mexico)
  • Chiara Ghidini (Fondazione Bruno Kessler, Italy)
  • Gianluigi Greco (University of Calabria, Italy)
  • Daniela Grigori (Laboratoire LAMSADE, University Paris-Dauphine, France)
  • Antonella Guzzo (University of Calabria, Italy)
  • Mieke Jans (Hasselt University, Belgium)
  • Gert Janssenswillen (Hasselt University, Belgium)
  • Anna Kalenkova (The University of Melbourne, Australia)
  • Christopher Klinkmüller (Data61|CSIRO, Australia)
  • Agnes Koschmider (Kiel University, Germany)
  • Marcello La Rosa (The University of Melbourne, Australia)
  • Manuel Lama Penin (University of Santiago de Compostela, Spain)
  • Sander J.J. Leemans (Queensland University of Technology, Australia)
  • Henrik Leopold (Kühne Logistics University, Germany)
  • Irina Lomazova (National Research University Higher School of Economics, Russia)
  • Xixi Lu (Utrecht University, the Netherlands)
  • Fabrizio Maria Maggi (Free University of Bozen-Bolzano, Italy)
  • Felix Mannhardt (Norwegian University of Science and Technology, Norway)
  • Andrea Marrella (Sapienza University of Rome, Italy)
  • Niels Martin (Hasselt University, Belgium)
  • Raimundas Matulevičius (University of Tartu, Estonia)
  • Massimo Mecella (Sapienza University of Rome, Italy)
  • Jan Mendling (Wirtschaftsuniversität Wien, Austria)
  • Marco Montali (Free University of Bolzano-Bozen, Italy)
  • Hamid Motahari (EY AI Lab Palo Alto, USA)
  • Jorge Munoz-Gama (Pontificia Universidad Católica de Chile, Chile)
  • Nicolò Navarin (University of Padua, Italy)
  • Artem Polyvyanyy (The University of Melbourne, Australia)
  • Luigi Pontieri (National Research Council of Italy – CNR, Italy)
  • Luise Pufahl (Humboldt-Universität zu Berlin, Germany)
  • Hajo A. Reijers (Utrecht University, the Netherlands)
  • Manuel Resinas (University of Seville, Spain)
  • Stefanie Rinderle-Ma (University of Vienna, Austria)
  • Arik Senderovich (University of Toronto, Canada)
  • Marcos Sepúlveda (Pontificia Universidad Católica de Chile, Chile)
  • Tijs Slaats (University of Copenhagen, Denmark)
  • Minseok Song (Pohang University of Science and Technology, South Korea)
  • Arthur ter Hofstede (Queensland University of Technology, Australia)
  • Han van der Aa (University of Mannheim, Germany)
  • Wil van der Aalst (RWTH Aachen University, Germany)
  • Boudewijn van Dongen (Eindhoven University of Technology, the Netherlands)
  • Sebastiaan J. van Zelst (Fraunhofer Institute – FIT & RWTH Aachen University, Germany)
  • Seppe Vanden Broucke (Katholieke Universiteit Leuven, Belgium)
  • Eric Verbeek (Eindhoven University of Technology, the Netherlands)
  • Jianmin Wang (Tsinghua University, China)
  • Barbara Weber (University of St. Gallen, Switzerland)
  • Ingo Weber (TU Berlin, Germany)
  • Matthias Weidlich (Humboldt-Universität zu Berlin, Germany)
  • Moe Thandar Wynn (Queensland University of Technology, Australia)