Diagnosing and Repairing Data Anomalies in Process Models

Diagnosing and RepairingData Anomalies in Process ModelsAhmed AwadHassoPlattner Institute, Potsdam, GermanyGero Decker HassoPlattner Institute, Potsdam, GermanyNielsLohmann University of Rostock, Germany

Correctness of Process Models
widely accepted: soundness
no deadlocks
no livelocks
proper termination
no dead activities
These are control flow aspects!
Diagnosing and Repairing Data Anomalies in Process Models
07.09.09

Example Process: Insurance Claim Handling
sound: every claim will eventually be closed
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Data in BPMN
Data objects
Data states (no explosion)
Object life cycles /control flow refinement
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Process Model with Data
This model contains five deadlocks!
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Classes of Errors
too restrictive preconditions (close and fraudulent claims)
implicit routing (XOR vs. fraud evaluation)
implicit execution order (pay vs. file)
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Outline
control flow + data flow = interesting problems ✔
formalization BPMN’s data aspects
detection, diagnosing, and repairing of data anomalies
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2
FormalizationBPMN’s dataaspects

BPMN and Petri nets
BPMN:
a graphical notion
support of concurrency
Petri nets:
a graphical notion
formal foundation
broad tool support
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Dijkman et al. definePetri net semanticsfor BPMN’s control flow

Petri net formalization (control flow)
pattern-based translation
complete example (control flow):
analysis tools can check soundness
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BPMN and Petri nets (2)
BPMN:
a graphical notion
several aspects in one model
Petri nets:
a graphical notion
formal foundation
broad tool support
simple composition notions
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Formalization of BPMN’s data objects
changing a state
reading a state
changing to several possible states
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Data flow models
data flow models for settlement and claim data object
control flow model
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Bringing it all together
synchronization of data flow and control flow by transition fusion
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3
Detection,diagnosing,and repair ofdata anomalies

Detection of data anomalies
standard soundness checker (Woflan, LoLA) will find deadlocks
provides counterexample (= trace)
does not differentiate data flow and control flow
gives no diagnosis/repair information
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Diagnosis data anomalies
exploit information on model:
control flow is sound
place models either control flow or data flow
each data object can only be in one state
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Diagnosing and fixing too restrictive preconditions
Problem: if data is set to [a], activity B is disabled
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Diagnosing and fixing too restrictive preconditions
control flow is sound
deadlock in composite model: missing data tokens
for each deadlock:
determine missing data tokens
change model
data tokens are present, or
drop data dependency
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Diagnosing and fixing implicit routing
decision [b] vs. [c] has to be synchronized with XOR-split
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Diagnosing and fixing implicit routing
partition state space with respect to data states
if a decision inside a partition leads to a deadlock, this decision is “unsynchronized”
synchronize decisions according to data
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Diagnosing and fixing implicit execution order
transitions A and B are in concurrent the control flow model,but share data place
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Take home points
data objects can introduce errors to a model
Petri nets allow for compositional models of data and control flow
data anomalies can be detected, diagnosed,and (sometimes) automatically fixed
Future work:
automated mapping back to the BPMN model
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Final slide
Thank you for your attention!
Your todos:
discuss with me
talk to Ahmed and Gero
attend the soundness talk(Thursday, after the keynote)
get the slides athttp://slideshare.net/correctsystems
07.09.09

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Diagnosing and Repairing Data Anomalies in Process Models

by Universität Rostock on Sep 07, 2009

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Diagnosing and Repairing Data Anomalies in Process Models Presentation Transcript