Andrea Burattin

Process Mining Techniques in Business Environments

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

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Business Information Processing 207
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-17481-5
• 2015
• Seitenzahl: 232
• Erscheinungstermin: 15. Mai 2015
• Englisch
• Abmessung: 235mm x 155mm x 13mm
• Gewicht: 359g
• ISBN-13: 9783319174815
• ISBN-10: 3319174819
• Artikelnr.: 42390740

Inhaltsangabe

1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.