Process Mining. ^J Springer. Discovery, Conformance and Enhancement of Business Processes. Wil M.R van der Aalst Q UNIVERS1TAT.

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1 Wil M.R van der Aalst Process Mining Discovery, Conformance and Enhancement of Business Processes Q UNIVERS1TAT m LIECHTENSTEIN Bibliothek ^J Springer

2 Contents 1 Introduction I 1.1 Data Explosion I 1.2 Limitations of Modeling Process Mining Analyzing an Example Log Play-in, Play-out, and Replay Trends Outlook 23 Part I Preliminaries 2 Process Modeling and Analysis The Art of Modeling Process Models Transition Systems Petri Nets Workflow Nets YAWL Business Process Modeling Notation (BPMN) Event-Driven Process Chains (EPCs) Causal Nets Model-Based Process Analysis Verification Performance Analysis Limitations of Model-Based Analysis 57 3 Data Mining Classification of Data Mining Techniques Data Sets: Instances and Variables Supervised Learning: Classification and Regression Unsupervised Learning: Clustering and Pattern Discovery Decision Tree Learning 64

3 xiv Contents Part II 3.3 -Means Clustering Association Rule Learning Sequence and Episode Mining Sequence Mining Episode Mining Other Approaches Quality of Resulting Models Measuring the Performance of a Classifier Cross-Validalion Occam's Razor 88 From Event Logs to Process Models 4 Getting the Data Data Sources Event Logs XES Flattening Reality into Event Logs Process Discovery: An Introduction Problem Statement A Simple Algorithm for Process Discovery Basic Idea Algorithm Limitations of the or-algorithm Taking the Transactional Life-Cycle into Account Rediscovering Process Models Challenges Representational Bias Noise and Incompleteness Four Competing Quality Criteria Taking the Right 2-D Slice of a 3-D Reality Advanced Process Discovery Techniques Overview Characteristic 1: Representational Bias Characteristic 2: Ability to Deal with Noise Characteristic 3: Completeness Notion Assumed Characteristic 4: Approach Used Heuristic Mining Causal Nets Revisited Learning the Dependency Graph Learning Splits and Joins Genetic Process Mining Region-Based Mining Learning Transition Systems Process Discovery Using State-Based Regions 177

4 Contents xv Process Discovery Using Language-Based Regions Historical Perspective 183 Part III Beyond Process Discovery 7 Conformance Checking Business Alignment and Auditing Token Replay Comparing Footprints Other Applications of Conformance Checking Repairing Models Evaluating Process Discovery Algorithms Connecting Event Log and Process Model Mining Additional Perspectives Perspectives Attributes: A Helicopter View Organizational Mining Social Network Analysis Discovering Organizational Structures Analyzing Resource Behavior Time and Probabilities Decision Mining Bringing It All Together Operational Support Refined Process Mining Framework Cartography Auditing Navigation Online Process Mining Delect Predict Recommend Process Mining Spectrum 258 Part IV Putting Process Mining to Work 10 Tool Support Business Intelligence?, ProM Other Process Mining Tools Outlook Analyzing "Lasagna Processes" Characterization of "Lasagna Processes" Use Cases Approach 282

5 xvi Contents Stage 0: Plan and Justify Stage 1: Extract Stage 2: Create Control-Flow Model and Connect Event Log Stage 3: Create Integrated Process Model Stage 4: Operational Support Applications Process Mining Opportunities per Functional Area Process Mining Opportunities per Sector Two Lasagna Processes Analyzing "Spaghetti Processes" Characterization of "Spaghetti Processes" Approach Applications Process Mining Opportunities for Spaghetti Processes Examples of Spaghetti Processes 310 Part V Reflection 13 Cartography and Navigation Business Process Maps Map Quality Aggregation and Abstraction Seamless Zoom Size, Color, and Layout Customization Process Mining: TomTom for Business Processes? Projecting Dynamic Information on Business Process Maps Arrival Time Prediction Guidance Rather than Control Epilogue Process Mining: A Bridge Between Data Mining and Business Process Management Challenges Start Today! 340 References.341 Index 349

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