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Process Mining Data Science in Action

:	Wil M.P. van der Aalst
:	Process Mining Data Science in Action
:	Springer-Verlag
:	9783662498514
:	2
:	CHF 47.50
:

:	Anwendungs-Software
:	English

:	477
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This is the second edition of Wil van der Aalst's seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics.

After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges.

O erall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Wil van der Aalst is a full professor at the Department of Mathematics& Computer Science of the Technische Universiteit Eindhoven (TU/e), The Netherlands, where he chairs the Architecture of Information Systems (AIS) group and serves as the scientific director of the Data Science Center Eindhoven. He also has a part-time appointment in the BPM group of Queensland University of Technology (QUT), Australia. His research and teaching interests include information systems, business process management, process modeling, Petri nets, process mining, and simulation.
Wil has published more than 180 journal papers, 19 books, 425 refereed conference or workshop publications, and 60 book chapters. Many of his papers are highly cited (he has a H-index of more than 123 according to Google Scholar, the highest among all European computer scientists) and his ideas on process support have influenced researchers, software developers, and standardization committees worldwide.

	Process Mining	3
	Preface	6
	Acknowledgements	9
	Contents	13
	Part I: Introduction	18
	Chapter 1: Data Science in Action	20
	1.1 Internet of Events	20
	1.2 Data Scientist	27
	1.3 Bridging the Gap Between Process Science and Data Science	32
	1.4 Outlook	37
	Chapter 2: Process Mining: The Missing Link	41
	2.1 Limitations of Modeling	41
	2.2 Process Mining	46
	2.3 Analyzing an Example Log	51
	2.4 Play-In, Play-Out, and Replay	57
	2.5 Positioning Process Mining	60
	2.5.1 How Process Mining Compares to BPM	60
	2.5.2 How Process Mining Compares to Data Mining	62
	2.5.3 How Process Mining Compares to Lean Six Sigma	62
	2.5.4 How Process Mining Compares to BPR	65
	2.5.5 How Process Mining Compares to Business Intelligence	65
	2.5.6 How Process Mining Compares to CEP	66
	2.5.7 How Process Mining Compares to GRC	66
	2.5.8 How Process Mining Compares to ABPD, BPI, WM, …	67
	2.5.9 How Process Mining Compares to Big Data	68
	Part II: Preliminaries	69
	Chapter 3: Process Modeling and Analysis	71
	3.1 The Art of Modeling	71
	3.2 Process Models	73
	3.2.1 Transition Systems	74
	3.2.2 Petri Nets	75
	3.2.3 Work?ow Nets	81
	3.2.4 YAWL	82
	3.2.5 Business Process Modeling Notation (BPMN)	84
	3.2.6 Event-Driven Process Chains (EPCs)	86
	3.2.7 Causal Nets	88
	3.2.8 Process Trees	94
	3.3 Model-Based Process Analysis	99
	3.3.1 Veri?cation	99
	3.3.2 Performance Analysis	101
	3.3.3 Limitations of Model-Based Analysis	104
	Chapter 4: Data Mining	105
	4.1 Classi?cation of Data Mining Techniques	105
	4.1.1 Data Sets: Instances and Variables	106
	4.1.2 Supervised Learning: Classi?cation and Regression	108
	4.1.3 Unsupervised Learning: Clustering and Pattern Discovery	110
	4.2 Decision Tree Learning	110
	4.3 k-Means Clustering	116
	4.4 Association Rule Learning	120
	4.5 Sequence and Episode Mining	123
	4.5.1 Sequence Mining	123
	4.5.2 Episode Mining	125
	4.5.3 Other Approaches	127
	4.6 Quality of Resulting Models	128
	4.6.1 Measuring the Performance of a Classi?er	129
	4.6.2 Cross-Validation	131
	4.6.3 Occam's Razor	134
	Part III: From Event Logs to Process Models	138
	Chapter 5: Getting the Data	140
	5.1 Data Sources	140
	5.2 Event Logs	143
	5.3 XES	153
	5.4 Data Quality	159
	5.4.1 Conceptualizing Event Logs	160
	5.4.2 Classi?cation of Data Quality Issues	163
	5.4.3 Guidelines for Logging	166
	5.5 Flattening Reality into Event Logs	168
	Chapter 6: Process Discovery: An Introduction	178
	6.1 Problem Statement	178
	6.2 A Simple Algorithm for Process Discovery	182
	6.2.1 Basic Idea	182
	6.2.2 Algorithm	186
	6.2.3 Limitations of the alpha-Algorithm	189
	6.2.4 Taking the Transactional Life-Cycle into Account	192
	6.3 Rediscovering Process Models	193
	6.4 Challenges	197
	6.4.1 Representational Bias	198
	6.4.2 Noise and Incompleteness	200
	6.4.2.1 Noise	200
	6.4.2.2 Incompleteness	201
	6.4.2.3 Cross-Validation	202
	6.4.3 Four Competing Quality Criteria	203
	6.4.4 Taking the Right 2-D Slice of a 3-D Reality	207
	Chapter 7: Advanced Process Discovery Techniques	210
	7.1 Overview	210
	7.1.1 Characteristic 1: Representational Bias	212
	7.1.2 Characteristic 2: Ability to Deal With Noise	213
	7.1.3 Characteristic 3: Completeness Notion Assumed	214
	7.1.4 Characteristic 4: