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Process Mining : Discovery, Conformance and Enhancement of Business Processes / by Wil M. P. van der Aalst
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2011Description: Online-Ressource (XVI, 352p. 184 illus., 6 illus. in color, digital)ISBN:- 9783642193453
- Prozessmanagement
- Betriebliches Informationssystem
- Data Mining
- Prozessmodell
- Logic design
- Information systems
- Management information systems
- Information storage and retrieval
- Business—Data processing
- Computer logic
- Computer Science
- Computer science
- Information storage and retrieval systems
- Software engineering
- Information technology
- Application software
- Business information services
- 622/.34 622/.34/0286
- 005.7
- 005.3 23
- QA76.76.A65
- TS156.8
Contents:
Summary: " More and more information about business processes is recorded by information systems in the form of so-called ""event logs"". Despite the omnipresence of such data, most organizations diagnose problems based on fiction rather than facts. Process mining is an emerging discipline based on process model-driven approaches and data mining. It not only allows organizations to fully benefit from the information stored in their systems, but it can also be used to check the conformance of processes, detect bottlenecks, and predict execution problems.Wil van der Aalst delivers the first book on process mining. It aims to be self-contained while covering the entire process mining spectrum from process discovery to operational support. In Part I, the author provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Part II focuses on process discovery as the most important process mining task. Part III moves beyond discovering the control flow of processes and highlights conformance checking, and organizational and time perspectives. Part IV guides the reader in successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM. Finally, Part V 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. "PPN: PPN: 1650903782Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS | ZDB-2-SEB
Process Mining; Preface; Acknowledgements; Contents; Chapter 1: Introduction; 1.1 Data Explosion; 1.2 Limitations of Modeling; 1.3 Process Mining; 1.4 Analyzing an Example Log; 1.5 Play-in, Play-out, and Replay; 1.6 Trends; 1.7 Outlook; Part I: Preliminaries; Chapter 2: Process Modeling and Analysis; 2.1 The Art of Modeling; 2.2 Process Models; 2.2.1 Transition Systems; 2.2.2 Petri Nets; 2.2.3 Workflow Nets; 2.2.4 YAWL; 2.2.5 Business Process Modeling Notation (BPMN); 2.2.6 Event-Driven Process Chains (EPCs); 2.2.7 Causal Nets; 2.3 Model-Based Process Analysis; 2.3.1 Verification
2.3.2 Performance Analysis2.3.3 Limitations of Model-Based Analysis; Chapter 3: Data Mining; 3.1 Classification of Data Mining Techniques; 3.1.1 Data Sets: Instances and Variables; 3.1.2 Supervised Learning: Classification and Regression; 3.1.3 Unsupervised Learning: Clustering and Pattern Discovery; 3.2 Decision Tree Learning; 3.3 k-Means Clustering; 3.4 Association Rule Learning; 3.5 Sequence and Episode Mining; 3.5.1 Sequence Mining; 3.5.2 Episode Mining; 3.5.3 Other Approaches; 3.6 Quality of Resulting Models; 3.6.1 Measuring the Performance of a Classifier; 3.6.2 Cross-Validation
3.6.3 Occam's RazorPart II: From Event Logs to Process Models; Chapter 4: Getting the Data; 4.1 Data Sources; 4.2 Event Logs; 4.3 XES; 4.4 Flattening Reality into Event Logs; Chapter 5: Process Discovery: An Introduction; 5.1 Problem Statement; 5.2 A Simple Algorithm for Process Discovery; 5.2.1 Basic Idea; 5.2.2 Algorithm; 5.2.3 Limitations of the alpha-Algorithm; 5.2.4 Taking the Transactional Life-Cycle into Account; 5.3 Rediscovering Process Models; 5.4 Challenges; 5.4.1 Representational Bias; 5.4.2 Noise and Incompleteness; 5.4.2.1 Noise; 5.4.2.2 Incompleteness; 5.4.2.3 Cross-Validation
5.4.3 Four Competing Quality Criteria5.4.4 Taking the Right 2-D Slice of a 3-D Reality; Chapter 6: Advanced Process Discovery Techniques; 6.1 Overview; 6.1.1 Characteristic 1: Representational Bias; 6.1.2 Characteristic 2: Ability to Deal with Noise; 6.1.3 Characteristic 3: Completeness Notion Assumed; 6.1.4 Characteristic 4: Approach Used; 6.1.4.1 Direct Algorithmic Approaches; 6.1.4.2 Two-Phase Approaches; 6.1.4.3 Computational Intelligence Approaches; 6.1.4.4 Partial Approaches; 6.2 Heuristic Mining; 6.2.1 Causal Nets Revisited; 6.2.2 Learning the Dependency Graph
6.2.3 Learning Splits and Joins6.3 Genetic Process Mining; 6.4 Region-Based Mining; 6.4.1 Learning Transition Systems; 6.4.2 Process Discovery Using State-Based Regions; 6.4.3 Process Discovery Using Language-Based Regions; 6.5 Historical Perspective; Part III: Beyond Process Discovery; Chapter 7: Conformance Checking; 7.1 Business Alignment and Auditing; 7.2 Token Replay; 7.3 Comparing Footprints; 7.4 Other Applications of Conformance Checking; 7.4.1 Repairing Models; 7.4.2 Evaluating Process Discovery Algorithms; 7.4.3 Connecting Event Log and Process Model
Chapter 8: Mining Additional Perspectives
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