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Process Mining - Chapter 1 - Introduction

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Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.

Published in: Business, Technology

Process Mining - Chapter 1 - Introduction

  1. 1. Chapter 1Introductionprof.dr.ir. Wil van der Aalstwww.processmining.org
  2. 2. OverviewChapter 1IntroductionPart I: PreliminariesChapter 2 Chapter 3Process Modeling and Data MiningAnalysisPart II: From Event Logs to Process ModelsChapter 4 Chapter 5 Chapter 6Getting the Data Process Discovery: An Advanced Process Introduction Discovery TechniquesPart III: Beyond Process DiscoveryChapter 7 Chapter 8 Chapter 9Conformance Mining Additional Operational SupportChecking PerspectivesPart IV: Putting Process Mining to WorkChapter 10 Chapter 11 Chapter 12Tool Support Analyzing “Lasagna Analyzing “Spaghetti Processes” Processes”Part V: ReflectionChapter 13 Chapter 14Cartography and EpilogueNavigation PAGE 1
  3. 3. Data explosion PAGE 2
  4. 4. The Worlds Technological Capacity to Store, Communicate, and ComputeInformation by Martin Hilbert and Priscila López (DOI 10.1126/science.1200970) PAGE 3
  5. 5. PAGE 4
  6. 6. Example process model examine thoroughly c1 c3 pay compensation examinestart register casually decide c5 end request c2 c4 reject check ticket request reinitiate request PAGE 5
  7. 7. Same process in terms of BPMN rather than Petri nets examine thoroughly pay examine compensation casually register decide requeststart reject end check ticket request reinitiate request PAGE 6
  8. 8. What are process models used for?• insight: while making a model, the modeler is triggered to view the process from various angles;• discussion: the stakeholders use models to structure discussions;• documentation: processes are documented for instructing people or certification purposes (cf. ISO 9000 quality management);• verification: process models are analyzed to find errors in systems or procedures (e.g., potential deadlocks);• performance analysis: techniques like simulation can be used to understand the factors influencing response times, service levels, etc.;• animation: models enable end users to “play out” different scenarios and thus provide feedback to the designer;• specification: models can be used to describe a PAIS before it is implemented and can hence serve as a “contract” between the developer and the end user/management; and• configuration: models can be used to configure a system. PAGE 7
  9. 9. Limitations• Executable models may be used to force people to work in a particular manner.• However, most models are not well-aligned with reality.• Most hand-made models are disconnected from reality and provide only an idealized view on the processes at hand: “paper tigers”.• Given (a) the interest in process models, (b) the abundance of event data, and (c) the limited quality of hand-made models, it seems worthwhile to relate event data to process models: process mining! PAGE 8
  10. 10. BPM life-cycle showing the classicaluses of process models diagnosis/ requirementsadjustment insight discussion performance animation analysis enactment/ (re)design monitoring data models verification documentation specification configuration/ implementation configuration PAGE 9
  11. 11. The three main types of process mining:discovery, conformance, and enhancement supports/ “world” business controls processes software people machines system components organizations records events, e.g., messages, specifies transactions, models configures etc. analyzes implements analyzes discovery (process) event conformance model logs enhancement PAGE 10
  12. 12. Orthogonal: Perspectives• The control-flow perspective focuses on the control- flow, i.e., the ordering of activities.• The organizational perspective focuses on information about resources hidden in the log, i.e., which actors (e.g., people, systems, roles, and departments) are involved and how are they related.• The case perspective focuses on properties of cases, e.g., cases can also be characterized by the values of the corresponding data elements.• The time perspective is concerned with the timing and frequency of events. PAGE 11
  13. 13. Starting point: event log XES, MXML, SA-MXML, CSV, etc. PAGE 12
  14. 14. Simplified event log a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, and h = reject request PAGE 13
  15. 15. Processdiscovery b examine thoroughly g c1 c3 pay c compensation a examine estart register casually decide c5 end request h c2 d c4 reject check ticket request f reinitiate request PAGE 14
  16. 16. Another example b c1 examine c3 thoroughly a e hstart register decide c5 reject end request request d c2 check ticket c4 PAGE 15
  17. 17. Beyond discovery:conformance and enhancement supports/ “world” business controls processes software people machines system components organizations records events, e.g., messages, specifies transactions, models configures etc. analyzes implements analyzes discovery (process) event conformance model logs enhancement PAGE 16
  18. 18. Another event log b examine thoroughly g c1 c3 pay c compensation a examine estart register casually decide c5 end request h c2 d c4 reject check ticket request f reinitiate request PAGE 17
  19. 19. Extension The event log can be used to discover roles in the organization (e.g., groups of people with similar work patterns). These roles can be Performance information (e.g., the used to relate individuals and average time between two activities. subsequent activities) can be extracted from the event log and visualized on top of the model. Role A: Role E: Role M: Assistant Expert Manager Decision rules (e.g., a decision tree based on data known at the time a Pete Sue Sara particular choice was made) can be learned from the event log and used Mike Sean to annotated decisions. Ellen E b A examine thoroughly A g A M c1 c3 pay c compensation a examine e A start register casually A decide c5 end request h c2 d c4 M reject check ticket request f reinitiate request PAGE 18
  20. 20. Play-Out process model event log PAGE 19
  21. 21. Play-Inevent log process model PAGE 20
  22. 22. Replay • extended model showing times, frequencies, etc. • diagnostics • predictions • recommendationsevent log process model PAGE 21
  23. 23. Replay• Connecting models to real events is crucial!• Possible uses: − Conformance checking − Repairing models − Extending the model with frequencies and temporal information − Constructing predictive models − Operational support (prediction, recommendation, etc.) PAGE 22
  24. 24. Desire lines in process models PAGE 23
  25. 25. Trends and terms• Business Process Management (BPM)• Business Intelligence (BI)• Online Analytical Processing (OLAP)• Business Activity Monitoring (BAM)• Complex Event Processing (CEP)• Corporate Performance Management (CPM)• Visual Analytics (VA)• Predictive Analytics (PA)• Continuous Process Improvement (CPI)• Total Quality Management (TQM)• Six Sigma PAGE 24
  26. 26. Six Sigma• Six Sigma was originally developed by Motorola in the early 1980s.• DMAIC approach: − Define the problem and set targets, − Measure key performance indicators and collect data, − Analyze the data to investigate and verify cause-and- effect relationships, − Improve the current process based on this analysis, − Control the process to minimize deviations from the target. PAGE 25
  27. 27. [μ-6σ, μ+6σ] with a 1.5σ shift A process that “runs at Six Sigma” has only 3.4 defective cases per million cases, i.e., on average 99.9997% of the cases is handled properly. PAGE 26
  28. 28. Performance improvement versuscompliance• Organizations are also putting more emphasis on corporate governance, risk, and compliance.• Scandals (Enron, Tyco, Adelphia, Peregrine, WorldCom, etc.) have fueled interest in more rigorous auditing practices.• New legislation such as the Sarbanes-Oxley Act (SOX) of 2002 and the Basel II Accord of 2004 emerged as a result.• Importance of verifying whether organizations operate “within their boundaries” is increasing. PAGE 27
  29. 29. Outlook Chapter 1 Introduction Part I: Preliminaries Chapter 2 Chapter 3 Process Modeling and Data Mining Analysis Part II: From Event Logs to Process Models Chapter 4 Chapter 5 Chapter 6 Getting the Data Process Discovery: An Advanced Process Introduction Discovery Techniques Part III: Beyond Process Discovery Chapter 7 Chapter 8 Chapter 9 Conformance Mining Additional Operational Support Checking Perspectives Part IV: Putting Process Mining to Work Chapter 10 Chapter 11 Chapter 12 Tool Support Analyzing “Lasagna Analyzing “Spaghetti Processes” Processes” Part V: Reflection Chapter 13 Chapter 14 Cartography and Epilogue Navigation PAGE 28

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