Transcript Predictive Task Monitoring for Business Processes Cristina Cabanillas, Claudio Di Ciccio, Jan Mendling, and Anne Baumgrass 12th International Conference on Business Process Management Eindhoven,

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
[email protected]
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
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
Business processes in
transport domain
Continuous task
monitoring
SEITE 5
Continuous task
monitoring
SEITE 6
Continuous task monitoring
in multimodal transport
SEITE 7
Continuous task monitoring
in multimodal transport
Diversion
SEITE 8
Diversion airport
Flight diversion
Flight diversion is an example of
continuous task execution anomaly
Flight diversion
Flight diversion is an example of
continuous task execution anomaly
Which is going to be diverted?
Source: http://www.flightradar24.com/
Dealing with flight diversions
A real-life scenario
End
©
Start
SEITE 12
Dealing with flight diversions
A real-life scenario
©
SEITE 13
Dealing with flight diversions
A real-life scenario
©
SEITE 14
Dealing with flight diversions
A real-life scenario
©
SEITE 15
Dealing with flight diversions
A real-life scenario
©
SEITE 16
Dealing with flight diversions
A real-life scenario
©
SEITE 17
Dealing with flight diversions
A real-life scenario
©
SEITE 18
Dealing with flight diversions
A real-life scenario
©
SEITE 19
Dealing with flight diversions
A real-life scenario
©
SEITE 20
Dealing with flight diversions
A real-life scenario
©
SEITE 21
Dealing with flight diversions
A real-life scenario
©
SEITE 22
Dealing with flight diversions
A real-life scenario
©
SEITE 23
Dealing with flight diversions
A real-life scenario
©
SEITE 24
Dealing with flight diversions
A real-life scenario
©
SEITE 25
Dealing with flight diversions
A real-life scenario
©
SEITE 26
Dealing with flight diversions
A real-life scenario
©
Modern technology
comes
into play
SEITE 27
Dealing with flight diversions
A real-life scenario
©
SEITE 28
Motivation
Objective:
monitor the
continuous task
and, in case of anomalies,
raise an alert
at this time:
not at this time:
SEITE 29
©
Motivation
Objective:
monitor the
continuous task
and, in case of anomalies,
raise an alert
at this time:
not at this time:
SEITE 30
… within an automatic
integrated system
Monitorable task:
An example
Flight diversion is
the violation of this constraint:
the final position of the aeroplane
does not coincide with
the landing airport area
SEITE 31
Interval-based
progress features
 Features are extracted out of data
 Clustered into fixed-length time intervals
Gather flight
data events
along a time
interval
SEITE 32
Interpolate
attribute
values
Redo
System Architecture:
Which component does what
  

Evaluation scenario:
Flight diversion detection*
based on real flight data
Logs
Regular
Diverted
311
268
43
Total
119
98
21
Training
192
170
22
Testing
 Flight data gathered from FlightStats.com
 June-July 2013
 U.S. flights
 Publicly available
* Thanks to Han van der Aa for his contribution
Evaluation scenario:
Flight diversion detection*
based on real flight data
Logs
Regular
Diverted
311
268
43
Total
119
98
21
Training
192
170
22
Testing
 Interval progress metrics:

covered distance (position)

time

aircraft altitude

aircraft speed
 Progress from start:

gained distance from the take-off airport
 Progress to end:

gained distance to the landing airport
* Thanks to Han van der Aa for his contribution
Evaluation scenario:
results*
 Best F-score:
30’ for prediction
 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
21’ for prediction
* Thanks to Han van der Aa for his contribution
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
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
[email protected]