Aalst Process Mining

Data Science in Action
2. Auflage 2016
ISBN: 978-3-662-49851-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 477 Seiten

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. 
Overall, 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.

Autoren/Hrsg.

Aalst, Wil M. P. van der

Inhaltsverzeichnis

1;Process Mining;3
1.1;Preface;6
1.2;Acknowledgements;9
1.3;Contents;13
2;Part I: Introduction;18
2.1;Chapter 1: Data Science in Action;20
2.1.1;1.1 Internet of Events;20
2.1.2;1.2 Data Scientist;27
2.1.3;1.3 Bridging the Gap Between Process Science and Data Science;32
2.1.4;1.4 Outlook;37
2.2;Chapter 2: Process Mining: The Missing Link;41
2.2.1;2.1 Limitations of Modeling;41
2.2.2;2.2 Process Mining;46
2.2.3;2.3 Analyzing an Example Log;51
2.2.4;2.4 Play-In, Play-Out, and Replay;57
2.2.5;2.5 Positioning Process Mining;60
2.2.5.1;2.5.1 How Process Mining Compares to BPM;60
2.2.5.2;2.5.2 How Process Mining Compares to Data Mining;62
2.2.5.3;2.5.3 How Process Mining Compares to Lean Six Sigma;62
2.2.5.4;2.5.4 How Process Mining Compares to BPR;65
2.2.5.5;2.5.5 How Process Mining Compares to Business Intelligence;65
2.2.5.6;2.5.6 How Process Mining Compares to CEP;66
2.2.5.7;2.5.7 How Process Mining Compares to GRC;66
2.2.5.8;2.5.8 How Process Mining Compares to ABPD, BPI, WM, …;67
2.2.5.9;2.5.9 How Process Mining Compares to Big Data;68
3;Part II: Preliminaries;69
3.1;Chapter 3: Process Modeling and Analysis;71
3.1.1;3.1 The Art of Modeling;71
3.1.2;3.2 Process Models;73
3.1.2.1;3.2.1 Transition Systems;74
3.1.2.2;3.2.2 Petri Nets;75
3.1.2.3;3.2.3 Work?ow Nets;81
3.1.2.4;3.2.4 YAWL;82
3.1.2.5;3.2.5 Business Process Modeling Notation (BPMN);84
3.1.2.6;3.2.6 Event-Driven Process Chains (EPCs);86
3.1.2.7;3.2.7 Causal Nets;88
3.1.2.8;3.2.8 Process Trees;94
3.1.3;3.3 Model-Based Process Analysis;99
3.1.3.1;3.3.1 Veri?cation;99
3.1.3.2;3.3.2 Performance Analysis;101
3.1.3.3;3.3.3 Limitations of Model-Based Analysis;104
3.2;Chapter 4: Data Mining;105
3.2.1;4.1 Classi?cation of Data Mining Techniques;105
3.2.1.1;4.1.1 Data Sets: Instances and Variables;106
3.2.1.2;4.1.2 Supervised Learning: Classi?cation and Regression;108
3.2.1.3;4.1.3 Unsupervised Learning: Clustering and Pattern Discovery;110
3.2.2;4.2 Decision Tree Learning;110
3.2.3;4.3 k-Means Clustering;116
3.2.4;4.4 Association Rule Learning;120
3.2.5;4.5 Sequence and Episode Mining;123
3.2.5.1;4.5.1 Sequence Mining;123
3.2.5.2;4.5.2 Episode Mining;125
3.2.5.3;4.5.3 Other Approaches;127
3.2.6;4.6 Quality of Resulting Models;128
3.2.6.1;4.6.1 Measuring the Performance of a Classi?er;129
3.2.6.2;4.6.2 Cross-Validation;131
3.2.6.3;4.6.3 Occam's Razor;134
4;Part III: From Event Logs to Process Models;138
4.1;Chapter 5: Getting the Data;140
4.1.1;5.1 Data Sources;140
4.1.2;5.2 Event Logs;143
4.1.3;5.3 XES;153
4.1.4;5.4 Data Quality;159
4.1.4.1;5.4.1 Conceptualizing Event Logs;160
4.1.4.2;5.4.2 Classi?cation of Data Quality Issues;163
4.1.4.3;5.4.3 Guidelines for Logging;166
4.1.5;5.5 Flattening Reality into Event Logs;168
4.2;Chapter 6: Process Discovery: An Introduction;178
4.2.1;6.1 Problem Statement;178
4.2.2;6.2 A Simple Algorithm for Process Discovery;182
4.2.2.1;6.2.1 Basic Idea;182
4.2.2.2;6.2.2 Algorithm;186
4.2.2.3;6.2.3 Limitations of the alpha-Algorithm;189
4.2.2.4;6.2.4 Taking the Transactional Life-Cycle into Account;192
4.2.3;6.3 Rediscovering Process Models;193
4.2.4;6.4 Challenges;197
4.2.4.1;6.4.1 Representational Bias;198
4.2.4.2;6.4.2 Noise and Incompleteness;200
4.2.4.2.1;6.4.2.1 Noise;200
4.2.4.2.2;6.4.2.2 Incompleteness;201
4.2.4.2.3;6.4.2.3 Cross-Validation;202
4.2.4.3;6.4.3 Four Competing Quality Criteria;203
4.2.4.4;6.4.4 Taking the Right 2-D Slice of a 3-D Reality;207
4.3;Chapter 7: Advanced Process Discovery Techniques;210
4.3.1;7.1 Overview;210
4.3.1.1;7.1.1 Characteristic 1: Representational Bias;212
4.3.1.2;7.1.2 Characteristic 2: Ability to Deal With Noise;213
4.3.1.3;7.1.3 Characteristic 3: Completeness Notion Assumed;214
4.3.1.4;7.1.4 Characteristic 4: Approach Used;214
4.3.1.4.1;7.1.4.1 Direct Algorithmic Approaches;214
4.3.1.4.2;7.1.4.2 Two-Phase Approaches;214
4.3.1.4.3;7.1.4.3 Divide-and-Conquer Approaches;215
4.3.1.4.4;7.1.4.4 Computational Intelligence Approaches;215
4.3.1.4.5;7.1.4.5 Partial Approaches;216
4.3.2;7.2 Heuristic Mining;216
4.3.2.1;7.2.1 Causal Nets Revisited;216
4.3.2.2;7.2.2 Learning the Dependency Graph;217
4.3.2.3;7.2.3 Learning Splits and Joins;220
4.3.3;7.3 Genetic Process Mining;222
4.3.4;7.4 Region-Based Mining;227
4.3.4.1;7.4.1 Learning Transition Systems;227
4.3.4.2;7.4.2 Process Discovery Using State-Based Regions;231
4.3.4.3;7.4.3 Process Discovery Using Language-Based Regions;233
4.3.5;7.5 Inductive Mining;237
4.3.5.1;7.5.1 Inductive Miner Based on Event Log Splitting;237
4.3.5.2;7.5.2 Characteristics of the Inductive Miner;244
4.3.5.3;7.5.3 Extensions and Scalability;248
4.3.6;7.6 Historical Perspective;251
5;Part IV: Beyond Process Discovery;256
5.1;Chapter 8: Conformance Checking;258
5.1.1;8.1 Business Alignment and Auditing;258
5.1.2;8.2 Token Replay;261
5.1.3;8.3 Alignments;271
5.1.4;8.4 Comparing Footprints;278
5.1.5;8.5 Other Applications of Conformance Checking;283
5.1.5.1;8.5.1 Repairing Models;283
5.1.5.2;8.5.2 Evaluating Process Discovery Algorithms;284
5.1.5.3;8.5.3 Connecting Event Log and Process Model;287
5.2;Chapter 9: Mining Additional Perspectives;290
5.2.1;9.1 Perspectives;290
5.2.2;9.2 Attributes: A Helicopter View;292
5.2.3;9.3 Organizational Mining;296
5.2.3.1;9.3.1 Social Network Analysis;297
5.2.3.2;9.3.2 Discovering Organizational Structures;302
5.2.3.3;9.3.3 Analyzing Resource Behavior;303
5.2.4;9.4 Time and Probabilities;305
5.2.5;9.5 Decision Mining;309
5.2.6;9.6 Bringing It All Together;312
5.3;Chapter 10: Operational Support;316
5.3.1;10.1 Re?ned Process Mining Framework;316
5.3.1.1;10.1.1 Cartography;318
5.3.1.2;10.1.2 Auditing;319
5.3.1.3;10.1.3 Navigation;320
5.3.2;10.2 Online Process Mining;320
5.3.3;10.3 Detect;322
5.3.4;10.4 Predict;326
5.3.5;10.5 Recommend;331
5.3.6;10.6 Processes Are Not in Steady State!;333
5.3.6.1;10.6.1 Daily, Weekly and Seasonal Patterns in Processes;333
5.3.6.2;10.6.2 Contextual Factors;333
5.3.6.3;10.6.3 Concept Drift in Processes;335
5.3.7;10.7 Process Mining Spectrum;336
6;Part V: Putting Process Mining to Work;337
6.1;Chapter 11: Process Mining Software;339
6.1.1;11.1 Process Mining Not Included!;339
6.1.2;11.2 Different Types of Process Mining Tools;341
6.1.3;11.3 ProM: An Open-Source Process Mining Platform;345
6.1.3.1;11.3.1 Historical Context;345
6.1.3.2;11.3.2 Example ProM Plug-Ins;347
6.1.3.3;11.3.3 Other Non-commercial Tools;351
6.1.3.3.1;11.3.3.1 PMLAB;351
6.1.3.3.2;11.3.3.2 CoBeFra;351
6.1.3.3.3;11.3.3.3 RapidProM;352
6.1.4;11.4 Commercial Software;353
6.1.4.1;11.4.1 Available Products;353
6.1.4.2;11.4.2 Strengths and Weaknesses;359
6.1.4.2.1;11.4.2.1 Limited Support for Concurrency;359
6.1.4.2.2;11.4.2.2 Limited Support for Conformance Checking;361
6.1.4.2.3;11.4.2.3 Performance Perspective is Well Supported;362
6.1.4.2.4;11.4.2.4 Data Perspective Not in Models;362
6.1.4.2.5;11.4.2.5 Organizational Perspective;362
6.1.4.2.6;11.4.2.6 Growing Support for XES;363
6.1.4.2.7;11.4.2.7 Getting Event Data from Other Sources;363
6.1.4.2.8;11.4.2.8 Filtering;363
6.1.4.2.9;11.4.2.9 No Automatic Clustering;363
6.1.4.2.10;11.4.2.10 Reporting and Animation;364
6.1.4.2.11;11.4.2.11 Links to Other Tools;365
6.1.4.2.12;11.4.2.12 Operational Support;365
6.1.4.2.13;11.4.2.13 Scalability;365
6.1.5;11.5 Outlook;366
6.2;Chapter 12: Process Mining in the Large;367
6.2.1;12.1 Big Event Data;367
6.2.1.1;12.1.1 N = All;368
6.2.1.2;12.1.2 Hardware and Software Developments;370
6.2.1.2.1;12.1.2.1 In-Memory Databases and Analytics;373
6.2.1.2.2;12.1.2.2 Columnar Databases;374
6.2.1.2.3;12.1.2.3 Large-Scale Distributed File Systems;375
6.2.1.3;12.1.3 Characterizing Event Logs;378
6.2.2;12.2 Case-Based Decomposition;382
6.2.2.1;12.2.1 Conformance Checking Using Case-Based Decomposition;383
6.2.2.2;12.2.2 Process Discovery Using Case-Based Decomposition;384
6.2.3;12.3 Activity-Based Decomposition;387
6.2.3.1;12.3.1 Conformance Checking Using Activity-Based Decomposition;388
6.2.3.2;12.3.2 Process Discovery Using Activity-Based Decomposition;390
6.2.4;12.4 Process Cubes;392
6.2.5;12.5 Streaming Process Mining;395
6.2.6;12.6 Beyond the Hype;398
6.3;Chapter 13: Analyzing "Lasagna Processes";400
6.3.1;13.1 Characterization of "Lasagna Processes";400
6.3.2;13.2 Use Cases;404
6.3.3;13.3 Approach;405
6.3.3.1;13.3.1 Stage 0: Plan and Justify;406
6.3.3.2;13.3.2 Stage 1: Extract;408
6.3.3.3;13.3.3 Stage 2: Create Control-Flow Model and Connect Event Log;408
6.3.3.4;13.3.4 Stage 3: Create Integrated Process Model;409
6.3.3.5;13.3.5 Stage 4: Operational Support;409
6.3.4;13.4 Applications;410
6.3.4.1;13.4.1 Process Mining Opportunities per Functional Area;410
6.3.4.2;13.4.2 Process Mining Opportunities per Sector;411
6.3.4.3;13.4.3 Two Lasagna Processes;415
6.3.4.3.1;13.4.3.1 RWS Process;415
6.3.4.3.2;13.4.3.2 WOZ Process;417
6.4;Chapter 14: Analyzing "Spaghetti Processes";423
6.4.1;14.1 Characterization of "Spaghetti Processes";423
6.4.2;14.2 Approach;427
6.4.3;14.3 Applications;430
6.4.3.1;14.3.1 Process Mining Opportunities for Spaghetti Processes;430
6.4.3.2;14.3.2 Examples of Spaghetti Processes;432
6.4.3.2.1;14.3.2.1 ASML;432
6.4.3.2.2;14.3.2.2 Philips Healthcare;433
6.4.3.2.3;14.3.2.3 AMC Hospital;436
7;Part VI: Re?ection;440
7.1;Chapter 15: Cartography and Navigation;442
7.1.1;15.1 Business Process Maps;442
7.1.1.1;15.1.1 Map Quality;443
7.1.1.2;15.1.2 Aggregation and Abstraction;443
7.1.1.3;15.1.3 Seamless Zoom;445
7.1.1.4;15.1.4 Size, Color, and Layout;449
7.1.1.5;15.1.5 Customization;451
7.1.2;15.2 Process Mining: TomTom for Business Processes?;452
7.1.2.1;15.2.1 Projecting Dynamic Information on Business Process Maps;452
7.1.2.2;15.2.2 Arrival Time Prediction;455
7.1.2.3;15.2.3 Guidance Rather than Control;455
7.2;Chapter 16: Epilogue;457
7.2.1;16.1 Process Mining as a Bridge Between Data Mining and Business Process Management;457
7.2.2;16.2 Challenges;459
7.2.3;16.3 Start Today!;461
8;References;463
9;Index;473