Replaying History. prof.dr.ir. Wil van der Aalst

Transcription

1 Replaying History prof.dr.ir. Wil van der Aalst

2 Growth of data PAGE 1

3 Process Mining: Linking events to models PAGE 2

4 Where did we apply process mining? 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 3

5 Example: WMO Harderwijk Process related to the execution of Wet Maatschappelijke Ondersteuning (WMO) Harderwijk Handling WMO applications WMO: supporting citizens of municipalities (illness, handicaps, elderly, etc.). Examples: wheelchair, scootmobiel,... adaptation of house (elevator),... household help,... PAGE 4

6 Event log (796 applications, 5187 events) PAGE 5

7 Process discovered using Genetic Miner PAGE 6

8 Various representations PAGE 7

9 Fuzzy Miner PAGE 8

10 Seamless abstraction more detailed more abstract PAGE 9

11 Fuzzy Replay PAGE 10

12 Conformance checking using Replay = should not have happened but did = should have happened but did not PAGE 11

13 Performance analysis using Replay PAGE 12

14 Performance information based on Replay PAGE 13

15 Prediction based on Replay PAGE 14

16 Replay can be used for many types of analysis involving a model and a log PAGE 15

17 Play Out (Classical use of models) A B C D A E D A E D A C B D A B C D A E D A C B D A C B D PAGE 16

18 Play In (Process Discovery) ABCD ACBD AED ACBD AED ABCD a process discovery algorithm like the α algorithm PAGE 17

19 Replay A B C D PAGE 18

20 Replay can detect problems AC D Problem! token left behind Problem! missing token PAGE 19

21 Replay can extract timing information A 5 B 8 C 9 D PAGE 20

22 Bottlenecks and Predictions A 5 B 8 PAGE 21

23 Applications of Replay Offline ( post mortem data only) register and quantify deviations register and quantify bottlenecks enrich models with times, probabilities, etc. repair models Online (including pre mortem data) generate alerts when deviations or delays occur predict when deviations or delays occur support taking countermeasures workflow simulation for operational decision support (aka fast-forward button ) PAGE 22

24 Replay Challenges Duplicate tasks Silent steps Advanced routing constructs (e.g., OR join and cancellation) Hierarchical models (n:m relation log/model) cf. work of Arya Adriansyah and Boudewijn van Dongen PAGE 23

25 To conclude. WILL TOMORROW S AUDITOR BE A PROCESS MINER?

26 PAGE 25

27 Thanks! cf. Wil van der Aalst Peter van den Brand Boudewijn van Dongen Christian Günther Eric Verbeek Ana Karla Alves de Medeiros Anne Rozinat Minseok Song Ton Weijters Remco Dijkman Gianluigi Greco Antonella Guzzo Kristian Bisgaard Lassen Ronny Mans Jan Mendling Vladimir Rubin Nikola Trcka Irene Vanderfeesten Barbara Weber Lijie Wen Arya Adriansyah Carmen Bratosin Toon Calders Jorge Cardoso Ronald Crooy Florian Gottschalk Monique Jansen-Vullers Peter Khisa Wakholi Nicolas Knaak Sven Lambrechts Joyce Nakatumba Mariska Netjes Mykola Pechenizkiy Maja Pesic Hajo Reijers Stefanie Rinderle Domenico Saccà Helen Schonenberg Marc Voorhoeve Jianmin Wang Jan Martijn van der Werf Martin van Wingerden Jianhong Ye Huub de Beer Elena Casares Alina Chipaila Walid Gaaloul Martijn van Giessel Shaifali Gupta Thomas Hoffmann Peter Hornix René Kerstjens Ralf Kramer Wouter Kunst Laura Maruster Andriy Nikolov Adarsh Ramesh Jo Theunissen Kenny van Uden... PAGE 26

28 Relevant WWW sites promimport.sourceforge.net Not everything that counts can be counted, and not everything that can be counted counts (Einstein) PAGE 27

29 IEEE Task Force on Process Mining The IEEE Task Force on Process Mining was established in the context of the Data Mining Technical Committee (DMTC) of the Computational Intelligence Society (CIS) of the Institute of Electrical and Electronic Engineers. The goal of this Task Force is to promote the research, development, education and understanding of process mining: make end-users, developers, consultants, and researchers aware of the state-of-the-art in process mining, promote the use of process mining techniques and tools and stimulating new applications, play a role in standardization efforts for logging event data, the organization of tutorials, special sessions, workshops, panels, the organization of Conferences/Workshop with IEEE CIS Technical Co- Sponsorship, and publications in the form of special issues in journals, books, articles (e.g., in the IEEE Computational Intelligence Magazine). See PAGE 28

30 Key Publications A. Rozinat and W.M.P. van der Aalst. Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models. In Business Process Management 2005 Workshops, volume 3812 of Lecture Notes in Computer Science, pages Springer-Verlag, Berlin, A. Rozinat and W.M.P. van der Aalst. Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems, 33(1):64-95, W.M.P. van der Aalst, A.K. Alves de Medeiros, and A.J.M.M. Weijters. Process Equivalence: Comparing Two Process Models Based on Observed Behavior. In BPM 2006, volume 4102 of Lecture Notes in Computer Science, pages Springer-Verlag, Berlin, 2006 H. Schonenberg, B. Weber, B.F. van Dongen, and W.M.P. van der Aalst. Supporting Flexible Processes Through Recommendations Based on History. In BPM 2008, volume 5240 of Lecture Notes in Computer Science, pages Springer-Verlag, Berlin, W.M.P. van der Aalst. Using Process Mining to Generate Accurate and Interactive Business Process Maps. In BIS 2009 Workshops, volume 37 of Lecture Notes in Business Information Processing, pages Springer-Verlag, Berlin, W.M.P. van der Aalst, V. Rubin, B.F. van Dongen, E. Kindler, and C.W. Günther. Process Mining: A Two-Step Approach to Balance Between Underfitting and Overfitting. Software and Systems Modeling, W.M.P. van der Aalst, M.H. Schonenberg, and M. Song. Time Prediction Based on Process Mining. BPM Center Report BPM-09-04, BPMcenter.org, PAGE 29