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Models vs. Reality dr.ir. B.F. van Dongen Assistant Professor Eindhoven University of Technology [email protected]

Models vs. Reality

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Models vs. Reality. dr.ir . B.F. van Dongen Assistant Professor Eindhoven University of Technology b.f.v.dongen @ tue.nl. Process Mining. Discovering processes How do people behave? Compliance oriented Where and why do people deviate from standards / rules / regulations? - PowerPoint PPT Presentation

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Page 1: Models  vs.  Reality

Models vs. Reality

dr.ir. B.F. van DongenAssistant ProfessorEindhoven University of [email protected]

Page 2: Models  vs.  Reality

Process Mining

• Discovering processes• How do people behave?

• Compliance oriented• Where and why do people

deviate from standards / rules / regulations?

• Performance oriented• Where are bottlenecks

in my processes?

Page 3: Models  vs.  Reality

Aligning models to Observed Behavior

• Starting point for conformance checking is a process model and a log

What is the most likely execution of the model, corresponding to a trace observed in the log?

Page 4: Models  vs.  Reality

Introduction: Alignments

• Alignments are used for conformance checking

• Alignments are computed over a trace and a model:− A trace is a (partial) order of activities− A model is a labeled Petri labeled with activities

• An alignment explains exactly where deviations occur:− A synchronous move mean that an activity is in the log and a corresponding

transition was enabled in the model− A log move means that no corresponding activity is found in the model− A model move means that no corresponding activity appeared in the log

Page 5: Models  vs.  Reality

Example

model:

ABDE

……

log

Page 6: Models  vs.  Reality

Logged “A” aligns nicely to model

model:

ABDE

……

log

AA

Page 7: Models  vs.  Reality

Logged “B” aligns nicely to model

model:

ABDE

……

log

AA

BB

Page 8: Models  vs.  Reality

Logged “D” does not fit the model

model:

ABDE

……

log

AA

BB D

Page 9: Models  vs.  Reality

“C” was probably executed, but was not logged

model:

ABDE

……

log

AA

BB D

C

Page 10: Models  vs.  Reality

Logged “E” aligns nicely to model

model:

ABDE

……

log

AA

BB D

C E

E

Page 11: Models  vs.  Reality

Alignment shows where deviations occurred

model:

ABDE

……

log

AA

BB D

C E

E

Alignment:The best way to fit

the trace in the model

Page 12: Models  vs.  Reality

Alignments

• Alignments specify exactly where deviations occurred when comparing logs to models

• Alignments can be used for:• Fitness/precision computations• Performance analysis• Model repair• ...• Compliance analysis

Page 13: Models  vs.  Reality

Use of alignment techniques in compliance

13

elicit compliance rules

formalize compliance rules

compliance checking and analysis

implement compliance measures

compliance improvement ?

Page 14: Models  vs.  Reality

Automated compliance checking

business process

compliance requirement

diagnostic information

compliance

specificationcompliance checker

Page 15: Models  vs.  Reality

Automated compliance checking

business process

compliance requirement

diagnostic information

compliance checker

Log

compliance Petri net pattern

alignmentA B

F

F

B

Ƭ

Page 16: Models  vs.  Reality

Specifying Compliance Rules

compliance specifier

compliance checker

rule repositoryWhich compliance

pattern?

precise Petri net pattern

How to prune the Petri net pattern?

Log

Page 17: Models  vs.  Reality

Elicit Compliance RuleProM6 (www.promtools.org/prom6)

X-rayPatient registration othersPatient

registrationX-RayPatient registration

Compliance Checking Using Conformance Checking

Implementation

Page 18: Models  vs.  Reality

Conclusions

• Alignments provide a powerful method to explain where operational processes deviated from models

• Using the right models, alignments can detect (and predict) possible violations of compliance rules

• Alignments provide guarantees on non-deviating cases

Page 19: Models  vs.  Reality

Future directions

Current challenges:

1. Representation and extraction of multi-dimensional event data for deviation detection

2. Representation and management of deviations

3. Detection and diagnosis of deviations

4. Online, real time deviation prediction

5. Integration of prototypes applicable to high-volume data

6. Application on real-life cases

Page 20: Models  vs.  Reality

Questions

?