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Process Mining - Data Science in Action
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Process Mining - Data Science in Action
von: Wil M.P. van der Aalst
Springer-Verlag, 2016
ISBN: 9783662498514
477 Seiten, Download: 22112 KB
 
Format:  PDF
geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop

Typ: B (paralleler Zugriff)

 

 
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Inhaltsverzeichnis

  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: Approach Used 214  
              7.1.4.1 Direct Algorithmic Approaches 214  
              7.1.4.2 Two-Phase Approaches 214  
              7.1.4.3 Divide-and-Conquer Approaches 215  
              7.1.4.4 Computational Intelligence Approaches 215  
              7.1.4.5 Partial Approaches 216  
        7.2 Heuristic Mining 216  
           7.2.1 Causal Nets Revisited 216  
           7.2.2 Learning the Dependency Graph 217  
           7.2.3 Learning Splits and Joins 220  
        7.3 Genetic Process Mining 222  
        7.4 Region-Based Mining 227  
           7.4.1 Learning Transition Systems 227  
           7.4.2 Process Discovery Using State-Based Regions 231  
           7.4.3 Process Discovery Using Language-Based Regions 233  
        7.5 Inductive Mining 237  
           7.5.1 Inductive Miner Based on Event Log Splitting 237  
           7.5.2 Characteristics of the Inductive Miner 244  
           7.5.3 Extensions and Scalability 248  
        7.6 Historical Perspective 251  
  Part IV: Beyond Process Discovery 256  
     Chapter 8: Conformance Checking 258  
        8.1 Business Alignment and Auditing 258  
        8.2 Token Replay 261  
        8.3 Alignments 271  
        8.4 Comparing Footprints 278  
        8.5 Other Applications of Conformance Checking 283  
           8.5.1 Repairing Models 283  
           8.5.2 Evaluating Process Discovery Algorithms 284  
           8.5.3 Connecting Event Log and Process Model 287  
     Chapter 9: Mining Additional Perspectives 290  
        9.1 Perspectives 290  
        9.2 Attributes: A Helicopter View 292  
        9.3 Organizational Mining 296  
           9.3.1 Social Network Analysis 297  
           9.3.2 Discovering Organizational Structures 302  
           9.3.3 Analyzing Resource Behavior 303  
        9.4 Time and Probabilities 305  
        9.5 Decision Mining 309  
        9.6 Bringing It All Together 312  
     Chapter 10: Operational Support 316  
        10.1 Re?ned Process Mining Framework 316  
           10.1.1 Cartography 318  
           10.1.2 Auditing 319  
           10.1.3 Navigation 320  
        10.2 Online Process Mining 320  
        10.3 Detect 322  
        10.4 Predict 326  
        10.5 Recommend 331  
        10.6 Processes Are Not in Steady State! 333  
           10.6.1 Daily, Weekly and Seasonal Patterns in Processes 333  
           10.6.2 Contextual Factors 333  
           10.6.3 Concept Drift in Processes 335  
        10.7 Process Mining Spectrum 336  
  Part V: Putting Process Mining to Work 337  
     Chapter 11: Process Mining Software 339  
        11.1 Process Mining Not Included! 339  
        11.2 Different Types of Process Mining Tools 341  
        11.3 ProM: An Open-Source Process Mining Platform 345  
           11.3.1 Historical Context 345  
           11.3.2 Example ProM Plug-Ins 347  
           11.3.3 Other Non-commercial Tools 351  
              11.3.3.1 PMLAB 351  
              11.3.3.2 CoBeFra 351  
              11.3.3.3 RapidProM 352  
        11.4 Commercial Software 353  
           11.4.1 Available Products 353  
           11.4.2 Strengths and Weaknesses 359  
              11.4.2.1 Limited Support for Concurrency 359  
              11.4.2.2 Limited Support for Conformance Checking 361  
              11.4.2.3 Performance Perspective is Well Supported 362  
              11.4.2.4 Data Perspective Not in Models 362  
              11.4.2.5 Organizational Perspective 362  
              11.4.2.6 Growing Support for XES 363  
              11.4.2.7 Getting Event Data from Other Sources 363  
              11.4.2.8 Filtering 363  
              11.4.2.9 No Automatic Clustering 363  
              11.4.2.10 Reporting and Animation 364  
              11.4.2.11 Links to Other Tools 365  
              11.4.2.12 Operational Support 365  
              11.4.2.13 Scalability 365  
        11.5 Outlook 366  
     Chapter 12: Process Mining in the Large 367  
        12.1 Big Event Data 367  
           12.1.1 N = All 368  
           12.1.2 Hardware and Software Developments 370  
              12.1.2.1 In-Memory Databases and Analytics 373  
              12.1.2.2 Columnar Databases 374  
              12.1.2.3 Large-Scale Distributed File Systems 375  
           12.1.3 Characterizing Event Logs 378  
        12.2 Case-Based Decomposition 382  
           12.2.1 Conformance Checking Using Case-Based Decomposition 383  
           12.2.2 Process Discovery Using Case-Based Decomposition 384  
        12.3 Activity-Based Decomposition 387  
           12.3.1 Conformance Checking Using Activity-Based Decomposition 388  
           12.3.2 Process Discovery Using Activity-Based Decomposition 390  
        12.4 Process Cubes 392  
        12.5 Streaming Process Mining 395  
        12.6 Beyond the Hype 398  
     Chapter 13: Analyzing "Lasagna Processes" 400  
        13.1 Characterization of "Lasagna Processes" 400  
        13.2 Use Cases 404  
        13.3 Approach 405  
           13.3.1 Stage 0: Plan and Justify 406  
           13.3.2 Stage 1: Extract 408  
           13.3.3 Stage 2: Create Control-Flow Model and Connect Event Log 408  
           13.3.4 Stage 3: Create Integrated Process Model 409  
           13.3.5 Stage 4: Operational Support 409  
        13.4 Applications 410  
           13.4.1 Process Mining Opportunities per Functional Area 410  
           13.4.2 Process Mining Opportunities per Sector 411  
           13.4.3 Two Lasagna Processes 415  
              13.4.3.1 RWS Process 415  
              13.4.3.2 WOZ Process 417  
     Chapter 14: Analyzing "Spaghetti Processes" 423  
        14.1 Characterization of "Spaghetti Processes" 423  
        14.2 Approach 427  
        14.3 Applications 430  
           14.3.1 Process Mining Opportunities for Spaghetti Processes 430  
           14.3.2 Examples of Spaghetti Processes 432  
              14.3.2.1 ASML 432  
              14.3.2.2 Philips Healthcare 433  
              14.3.2.3 AMC Hospital 436  
  Part VI: Re?ection 440  
     Chapter 15: Cartography and Navigation 442  
        15.1 Business Process Maps 442  
           15.1.1 Map Quality 443  
           15.1.2 Aggregation and Abstraction 443  
           15.1.3 Seamless Zoom 445  
           15.1.4 Size, Color, and Layout 449  
           15.1.5 Customization 451  
        15.2 Process Mining: TomTom for Business Processes? 452  
           15.2.1 Projecting Dynamic Information on Business Process Maps 452  
           15.2.2 Arrival Time Prediction 455  
           15.2.3 Guidance Rather than Control 455  
     Chapter 16: Epilogue 457  
        16.1 Process Mining as a Bridge Between Data Mining and Business Process Management 457  
        16.2 Challenges 459  
        16.3 Start Today! 461  
  References 463  
  Index 473  


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