Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Process Mining - Chapter 13 - Cartography and Navigation

1,165 views

Published on

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
  • ➤➤ 3 Reasons Why You Shouldn't take Pills for ED (important) ■■■ https://tinyurl.com/rockhardxxx
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Process Mining - Chapter 13 - Cartography and Navigation

  1. 1. Chapter 13Cartography and Navigationprof.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. Business process maps The first geographical maps date back to the 7th Millennium BC. Since then cartographers have improved their skills and techniques to create maps thereby addressing problems such as clearly representing desired traits, eliminating irrelevant details, reducing complexity, and improving understandability. PAGE 2
  4. 4. Example of a map Road map of The Netherlands. The map abstracts from smaller cities and less significant roads; only the bigger cities, highways, and other important roads are shown. Moreover, cities aggregate local roads and local districts. Also not use of color, size, etc. PAGE 3
  5. 5. B A E Need for trip has arisen Entry of a C travel request Trip is requested Approval of travel request D Need Planned Planned to correct trip trip planned is rejected is approved trip is transmitted Advance payment Trip Unrequested Approved advance trip tripis transmitted/ has taken has taken paid place place Entry of trip facts Trip facts and receipts have been released for checking Approval of trip facts Planned Trip Trip Approval trip expenses facts of trip must reimbursement are released facts be canceled is rejected for accounting is transmitted Accounting date is reached Travel Expenses Trip Payment Trip Amounts Amounts Trip expenses amount costs Payments Payment relevant liable costs reimbursement transmitted must must must to accounting to employment statement must to bank/ be included be released be effected transmitted tax transmitted is transmitted be canceled payee in cost accounting to payroll accounting to payroll Cancellation Trip costs Trip cancelation is canceled statement is transmitted PAGE 4
  6. 6. More significant nodes are emphasizedHighlights moreimportant paths PAGE 5
  7. 7. More to learn from maps... Aggregation Abstraction Clustering of coherent, Removing isolated, less less significant structures significant structures PAGE 6
  8. 8. Illustrating the problemx start y 1.0 z 1.0 1.0 a f j p3 p9 p1 p12 p7 0.4 0.3 0.4 0.6 0.6 0.4 0.3 0.6 0.4 b c d g h k l0.4 0.3 0.3 p4 0.4 0.6 p10 p2 p8 p5 p11 1.0 e i 1.0 p6 end PAGE 7
  9. 9. Classical top level view: low level connections still exist p3 p9 p4 x y z p10 p5 p11x start y 1.0 z 1.0 p6 1.0 a f j p3 p9 p1 p12 p7 0.4 0.3 0.4 0.6 0.6 0.4 0.3 0.6 0.4 b c d g h k l0.4 0.3 0.3 p4 0.4 0.6 p10 p2 p8 p5 p11 1.0 e i 1.0 p6 end PAGE 8
  10. 10. Seamless zoomThreshold: 1.0 x y z a f j x y z e iThreshold: 0.6 x y z a f j h k x y z e iThreshold: 0.4 x y z a f j b g h k l x y z e iThreshold: 0.3 x y z a f j b c d g h k l x y z e i PAGE 9
  11. 11. Example: Reviewing papers(100 cases generating 3730 events) WF-net discovered using the α-algorithm PAGE 10
  12. 12. Fuzzy miner: two views on the same process fuzzy model showing fuzzy model all activities showing only two activities color and width of arc indicates significanceof connection PAGE 11
  13. 13. Balancing between both extremes fuzzy model showing all activities fuzzy model showing only two activities color and width of arc indicates significanceof connection aggregated node containing 10 activities inner structure of aggregated node PAGE 12
  14. 14. Not a single map! PAGE 13
  15. 15. Projecting dynamic information onbusiness process maps PAGE 14
  16. 16. Projecting traffic jams on maps PAGE 15
  17. 17. Business process movies PAGE 16
  18. 18. Navigation• Whereas a TomTom device is continuously showing the expected arrival time, users of today’s information systems are often left clueless about likely outcomes of the cases they are working on.• Car navigation systems provide directions and guidance without controlling the driver. The driver is still in control, but, given a goal (e.g. to get from A to B as fast as possible), the navigation system recommends the next action to be taken.• Operational support provides TomTom functionality for business processes. PAGE 17
  19. 19. Recommend: How to get home ASAP? Take a left turn! Detect: You drive too fast! Predict: When will I be home? At 11.26! PAGE 18
  20. 20. Relating the process mining frameworkto cartography and navigation people organizations machines business “world” processes documents information system(s) provenance event logs “pre “post mortem” current historic mortem” data datanavigation auditing cartography recommend diagnose compare enhance discover promote explore predict detect check models de jure models de facto models control-flow control-flow data/rules data/rules resources/ resources/ organization organization PAGE 19

×