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Process Mining Workshops

ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Sander Leemans

    1. Queensland University of Technology, Brisbane, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Henrik Leopold

    1. Kühne Logistics University, Hamburg, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Business Information Processing (LNBIP, volume 406)

Included in the following conference series:

Conference proceedings info: ICPM 2020.

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Table of contents (29 papers)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. 1st International Workshop on Event Data and Behavioral Analytics (EDBA)

    1. Front Matter

      Pages 1-3
      Download chapter PDF

    2. Visually Representing History Dependencies in Event Logs

      • Manuel Wetzel, Agnes Koschmider, Thomas Wilke
      Pages 5-16

    3. Analysis of Business Process Batching Using Causal Event Models

      • Philipp Waibel, Christian Novak, Saimir Bala, Kate Revoredo, Jan Mendling
      Pages 17-29

    4. Process “Prospecting” to Improve Renewable Energy Interconnection Queues: A Case Study

      • Gerry Murphy
      Pages 30-42

    5. Automated Discovery of Process Models with True Concurrency and Inclusive Choices

      • Adriano Augusto, Marlon Dumas, Marcello La Rosa
      Pages 43-56

    6. A Novel Approach to Discover Switch Behaviours in Process Mining

      • Yang Lu, Qifan Chen, Simon Poon
      Pages 57-68

    7. Process Model Discovery from Sensor Event Data

      • Dominik Janssen, Felix Mannhardt, Agnes Koschmider, Sebastiaan J. van Zelst
      Pages 69-81

    8. Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study

      • Greg Van Houdt, Benoît Depaire, Niels Martin
      Pages 82-93

  3. 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM)

    1. Front Matter

      Pages 95-98
      Download chapter PDF

    2. Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach

      • Fadi Baskharon, Ahmed Awad, Chiara Di Francescomarino
      Pages 99-111

    3. Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

      • An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern Eskofier
      Pages 112-123

    4. A Preliminary Study on the Application of Reinforcement Learning for Predictive Process Monitoring

      • Andrea Chiorrini, Claudia Diamantini, Alex Mircoli, Domenico Potena
      Pages 124-135

    5. An Alignment Cost-Based Classification of Log Traces Using Machine-Learning

      • Mathilde Boltenhagen, Benjamin Chetioui, Laurine Huber
      Pages 136-148

    6. Improving the Extraction of Process Annotations from Text with Inter-sentence Analysis

      • Luis Quishpi, Josep Carmona, Lluís Padró
      Pages 149-161

    7. Case2vec: Advances in Representation Learning for Business Processes

      • Stefan Luettgen, Alexander Seeliger, Timo Nolle, Max Mühlhäuser
      Pages 162-174

    8. Supervised Conformance Checking Using Recurrent Neural Network Classifiers

      • Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
      Pages 175-187

  4. 1st International Workshop on Streaming Analytics for Process Mining (SA4PM’20)

    1. Front Matter

      Pages 189-192
      Download chapter PDF

    2. Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events

      • Jonghyeon Ko, Marco Comuzzi
      Pages 193-205

    3. Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation

      • Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, Thomas Seidl
      Pages 206-217
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  1. Process Mining Workshops

Keywords

About this book

This book constitutes revised selected papers from the International Workshops held at the Second International Conference on Process Mining, ICPM 2020, which took place during October 4-9, 2020. The conference was planned to take place in Padua, Italy, but had to be held online due to the COVID-19 pandemic.

The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 29 papers included in this volume were carefully reviewed and selected from 59 submissions. They stem from the following workshops:

1st International Workshop on Event Data and Behavioral Analytics (EDBA)

1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM)

1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20)

5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI)

3rd International Workshop on Process-Oriented Data Science for Healthcare (PODS4H)

1st International Workshop on Trust and Privacy in Process Analytics (TPPA)

 

Editors and Affiliations

  • Queensland University of Technology, Brisbane, Australia

    Sander Leemans

  • Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

Bibliographic Information

  • Book Title: Process Mining Workshops

  • Book Subtitle: ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers

  • Editors: Sander Leemans, Henrik Leopold

  • Series Title: Lecture Notes in Business Information Processing

  • DOI: https://doi.org/10.1007/978-3-030-72693-5

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-72692-8Published: 31 March 2021

  • eBook ISBN: 978-3-030-72693-5Published: 30 March 2021

  • Series ISSN: 1865-1348

  • Series E-ISSN: 1865-1356

  • Edition Number: 1

  • Number of Pages: XIV, 400

  • Number of Illustrations: 51 b/w illustrations, 101 illustrations in colour

  • Topics: Data Mining and Knowledge Discovery

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