Keynote on Process Mining at SSCI 2010 / CIDM 2011

  • View
    365

  • Download
    2

Embed Size (px)

DESCRIPTION

Keynote IEEE Symposium Series on Computational Intelligence (SSCI 2011)/IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), April 2011, Paris, France

Text of Keynote on Process Mining at SSCI 2010 / CIDM 2011

  • 1. Process Mining:Discovering and ImprovingSpaghetti and Lasagna Processesprof.dr.ir. Wil van der Aalstwww.processmining.org
  • 2. Architecture of Information Systems @ TU/e process BPM/WFM/ discovery SOA systems Process PAIS Mining Technology conformance workflow checking patterns simulation Process Modeling/ Analysis verification
  • 3. Data explosion PAGE 2
  • 4. The Worlds Technological Capacity to Store, Communicate, and ComputeInformation by Martin Hilbert and Priscila Lpez (DOI 10.1126/science.1200970) PAGE 3
  • 5. Process Mining = (RM,RD) c11 modify conditions (YE,RD) check_A c5 (RM,RD) c2 check_A c8 (E,SD) needed? (RM,RD) (E,RD) Smoker c6(YE,RD) No start register c1 initial c3 check_B check_B c9 asses c12 decline conditions needed? risk Yes c7(FE,FD) Drinker c4 check_C check_C c10 needed? Short(91/10) 100 organizations Municipalities (e.g., Alkmaar, Heusden, Harderwijk, etc.) Government agencies (e.g., Rijkswaterstaat, Centraal Justitieel Incasso Bureau, Justice department) Insurance related agencies (e.g., UWV) Banks (e.g., ING Bank) Hospitals (e.g., AMC hospital, Catharina hospital) Multinationals (e.g., DSM, Deloitte) High-tech system manufacturers and their customers (e.g., Philips Healthcare, ASML, Ricoh, Thales) Media companies (e.g. Winkwaves) ... PAGE 6
  • 8. Process Mining 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
  • 9. Starting point: event log XES, MXML, SA-MXML, CSV, etc. PAGE 8
  • 10. 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 9
  • 11. 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 10
  • 12. Conformancechecking b case 7: e is executed examine without thoroughly case 8: g or being g h is missing enabled c1 c3 pay c compensation a examine estart register casually decide c5 end request case 10: e h d is missing c2 c4 reject in second check ticket round request f reinitiate request PAGE 11
  • 13. Extension: Adding perspectives tomodel based on event log 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