1. Predictive Task Monitoring for Business Processes Cristina Cabanillas, Claudio Di Ciccio, Jan Mendling, and Anne Baumgrass 12th International Conference on Business Process Management Eindhoven, The Netherlands claudio.di.ciccio@wu.ac.at

2. GET Service Project  FP7 EU Research Project  Aim of GET Service  Providing transportation planners and drivers of transportation vehicles with the means to plan, re-plan and control transportation routes…  … efficiently  … reducing CO2 emission  Partners: TU/e, Exodus, Hasso Plattner Institut, IBM Zurich Research Lab, Jan de Rijk Logistics, Portbase, PTV, TransVer, Wirtschaftsuniversität Wien SEITE 2

3. Outline  Context  Motivation scenario from logistics  Challenges and benefits of continuous task monitoring  Architecture for continuous event monitoring and anomaly detection  Evaluation over real data  Conclusions SEITE 3

4. Business processes in transport domain

5. Continuous task monitoring SEITE 5

6. Continuous task monitoring SEITE 6

7. Continuous task monitoring in multimodal transport SEITE 7

8. Continuous task monitoring in multimodal transport SEITE 8 Diversion Diversion airport

9. Flight diversion Flight diversion is an example of continuous task execution anomaly

10. Flight diversion Flight diversion is an example of continuous task execution anomaly

11. Which is going to be diverted? Source: http://www.flightradar24.com/

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27. Dealing with flight diversions A real-life scenario Modern technology comes into play SEITE 27 ©

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29. Objective: monitor the continuous task and, in case of anomalies, raise an alert at this time: not at this time: Motivation SEITE 29 ©

30. Motivation Objective: monitor the continuous task and, in case of anomalies, raise an alert at this time: not at this time: … within an automatic integrated system SEITE 30

31. Monitorable task: An example SEITE 31 Flight diversion is the violation of this constraint: the final position of the aeroplane does not coincide with the landing airport area

32. Interval-based progress features  Features are extracted out of data  Clustered into fixed-length time intervals SEITE 32 Gather flight data events along a time interval Interpolate attribute values Redo

33. System Architecture: Which component does what    

34. Evaluation scenario: Flight diversion detection* based on real flight data  Flight data gathered from FlightStats.com  June-July 2013  U.S. flights  Publicly available LogsRegularDiverted 31126843Total 1199821Training 19217022Testing * Thanks to Han van der Aa for his contribution

35. Evaluation scenario: Flight diversion detection* based on real flight data  Interval progress metrics:  covered distance (position)  time  aircraft altitude  aircraft speed LogsRegularDiverted 31126843Total 1199821Training 19217022Testing * Thanks to Han van der Aa for his contribution  Progress from start:  gained distance from the take-off airport  Progress to end:  gained distance to the landing airport

36. Evaluation scenario: results*  Best F-score:  87.8%  Time gain for predicted diversions:  104 minutes gained w.r.t. expected landing time  64 minutes gained w.r.t. actual landing time * Thanks to Han van der Aa for his contribution 21’ for prediction 30’ for prediction

37. Conclusions  What we presented:  Framework for continuous-task execution monitoring  Integrated system for disruption alerts  More in the paper:  Requirements leading to the proposed architecture  Design and implementation details for the Extended Discriminative Classifier  Definition of interval-based progress features  Future work:  Application of the framework to different task types and event information  Automated adjustment of thresholds for alerting

38. Predictive Task Monitoring for Business Processes Cristina Cabanillas, Claudio Di Ciccio, Jan Mendling, and Anne Baumgrass 12th International Conference on Business Process Management Eindhoven, The Netherlands claudio.di.ciccio@wu.ac.at