Process Mining Techniques in Business Environments

Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining

  • Andrea¬†Burattin

Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 207)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Andrea Burattin
    Pages 1-7
  3. Part I: State of the Art: BPM, Data Mining and Process Mining

    1. Front Matter
      Pages 9-9
    2. Andrea Burattin
      Pages 27-32
    3. Andrea Burattin
      Pages 33-47
    4. Andrea Burattin
      Pages 49-52
    5. Andrea Burattin
      Pages 53-55
  4. Part II: Obstacles to Process Mining in Practice

    1. Front Matter
      Pages 57-57
    2. Andrea Burattin
      Pages 65-68
  5. Part III: Process Mining as an Emerging Technology

    1. Front Matter
      Pages 69-69
    2. Andrea Burattin
      Pages 71-83
    3. Andrea Burattin
      Pages 85-95
    4. Andrea Burattin
      Pages 97-112
    5. Andrea Burattin
      Pages 113-118
    6. Andrea Burattin
      Pages 137-162
    7. Andrea Burattin
      Pages 163-174
  6. Part IV: A New Challenge in Process Mining

    1. Front Matter
      Pages 175-175
    2. Andrea Burattin
      Pages 177-204
  7. Part V: Conclusions and Future Work

    1. Front Matter
      Pages 205-205
    2. Andrea Burattin
      Pages 207-210
  8. Back Matter
    Pages 211-220

About this book


After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining."

The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.


BPM Business Process Management Data Mining Data Streams Process Mining

Authors and affiliations

  • Andrea¬†Burattin
    • 1
  1. 1.University of InnsbruckInnsbruckAustria

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-17481-5
  • Online ISBN 978-3-319-17482-2
  • Series Print ISSN 1865-1348
  • Series Online ISSN 1865-1356
  • Buy this book on publisher's site