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Process Mining : Data Science in Action.

Author: Wil Van der Aalst
Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint : Springer, 2016.
Edition/Format:   Print book : Document   Computer File : English : 2nd edView all editions and formats
Summary:
The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Van der Aalst, Wil.
Process mining : data science in action.
Berlin, [Germany] ; Heidelberg, [Germany] : Springer, ©2016
xix, 467 pages
Material Type: Document
Document Type: Book, Computer File
All Authors / Contributors: Wil Van der Aalst
ISBN: 3662498502 9783662498507 9783662498514 3662498510
OCLC Number: 990677015
Language Note: English.
Notes: 7.1.4.1 Direct Algorithmic Approaches.
Description: 1 online resource (477 pages)
Contents: Process Mining; Preface; Acknowledgements; Contents; Part I: Introduction; Chapter 1: Data Science in Action; 1.1 Internet of Events; 1.2 Data Scientist; 1.3 Bridging the Gap Between Process Science and Data Science; 1.4 Outlook; Chapter 2: Process Mining: The Missing Link; 2.1 Limitations of Modeling; 2.2 Process Mining; 2.3 Analyzing an Example Log; 2.4 Play-In, Play-Out, and Replay; 2.5 Positioning Process Mining; 2.5.1 How Process Mining Compares to BPM; 2.5.2 How Process Mining Compares to Data Mining; 2.5.3 How Process Mining Compares to Lean Six Sigma. 2.5.4 How Process Mining Compares to BPR2.5.5 How Process Mining Compares to Business Intelligence; 2.5.6 How Process Mining Compares to CEP; 2.5.7 How Process Mining Compares to GRC; 2.5.8 How Process Mining Compares to ABPD, BPI, WM ... ; 2.5.9 How Process Mining Compares to Big Data; Part II: Preliminaries; Chapter 3: Process Modeling and Analysis; 3.1 The Art of Modeling; 3.2 Process Models; 3.2.1 Transition Systems; 3.2.2 Petri Nets; 3.2.3 Work ow Nets; 3.2.4 YAWL; 3.2.5 Business Process Modeling Notation (BPMN); 3.2.6 Event-Driven Process Chains (EPCs); 3.2.7 Causal Nets. 3.2.8 Process Trees3.3 Model-Based Process Analysis; 3.3.1 Veri cation; 3.3.2 Performance Analysis; 3.3.3 Limitations of Model-Based Analysis; Chapter 4: Data Mining; 4.1 Classi cation of Data Mining Techniques; 4.1.1 Data Sets: Instances and Variables; 4.1.2 Supervised Learning: Classi cation and Regression; 4.1.3 Unsupervised Learning: Clustering and Pattern Discovery; 4.2 Decision Tree Learning; 4.3 k-Means Clustering; 4.4 Association Rule Learning; 4.5 Sequence and Episode Mining; 4.5.1 Sequence Mining; 4.5.2 Episode Mining; 4.5.3 Other Approaches; 4.6 Quality of Resulting Models. 4.6.1 Measuring the Performance of a Classi er4.6.2 Cross-Validation; 4.6.3 Occam's Razor; Part III: From Event Logs to Process Models; Chapter 5: Getting the Data; 5.1 Data Sources; 5.2 Event Logs; 5.3 XES; 5.4 Data Quality; 5.4.1 Conceptualizing Event Logs; 5.4.2 Classi cation of Data Quality Issues; 5.4.3 Guidelines for Logging; 5.5 Flattening Reality into Event Logs; Chapter 6: Process Discovery: An Introduction; 6.1 Problem Statement; 6.2 A Simple Algorithm for Process Discovery; 6.2.1 Basic Idea; 6.2.2 Algorithm; 6.2.3 Limitations of the alpha-Algorithm. 6.2.4 Taking the Transactional Life-Cycle into Account6.3 Rediscovering Process Models; 6.4 Challenges; 6.4.1 Representational Bias; 6.4.2 Noise and Incompleteness; 6.4.2.1 Noise; 6.4.2.2 Incompleteness; 6.4.2.3 Cross-Validation; 6.4.3 Four Competing Quality Criteria; 6.4.4 Taking the Right 2-D Slice of a 3-D Reality; Chapter 7: Advanced Process Discovery Techniques; 7.1 Overview; 7.1.1 Characteristic 1: Representational Bias; 7.1.2 Characteristic 2: Ability to Deal With Noise; 7.1.3 Characteristic 3: Completeness Notion Assumed; 7.1.4 Characteristic 4: Approach Used.
Other Titles: Process Mining 2016

Abstract:

The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool  Read more...

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"The author of the book, Wil van der Aalst, is very knowledgeable in the area. ... His research interests are workflow management, process mining, Petri nets, business process management, process Read more...

 
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