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On Application of Structural Decomposition for Process Model Abstraction Artem Polyvyanyy Sergey Smirnov Mathias Weske BPSC 2009     24 March 2009
Motivation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Business Process Model Abstraction ,[object Object]
Business Process Model Abstraction What model elements are insignificant? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],How to abstract insignificant elements?
Process Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregation vs. Elimination Aggregate Eliminate
Assumption: Sound Process Models Hidden deadlock Hidden unsafe process fragment unsound model sound model Assume initial models to be sound
Stepwise Abstraction
Order Preserving Abstraction F A A  and  B  belong to  F A , ordering constraints are lost C  and  D  do not belong to  F A , ordering constraints are preserved A  belong to  F A  and  D  does not, ordering constraints between F  and  D  as between  A  and  D
Single Entry Single Exit Fragment ,[object Object],[object Object],[object Object]
Canonical SESE Fragment ,[object Object],[object Object]
Relations between SESE Fragments p arent - child predecessor-successor if the node set of SESE fragment  f 1  is the subset of node set of SESE fragment  f 2 , then  f 1  is the child of  f 2  and  f 2  is the parent of  f 1 SESE fragment  f 1  precedes SESE fragment  f 2  (and  f 2  succeeds  f 1 ) if the outgoing edge of  f 1  is the incoming edge of  f 2 P 1 c 2 c 1 p 1 s 2 s 1 p 2
Process Structure Tree parent-child predecessor-successor
Auxiliary Concepts ,[object Object],[object Object],[object Object],[object Object],[object Object]
Abstraction Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Abstraction Smoothness smoothness = 2 smoothness = 2 smoothness = 5 …  loss of information is essential and desired …  abstraction smoothness quantitatively estimates the information loss produced by one abstraction step
Smoothness Evaluation (I) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Smoothness Evaluation (II) „ Optimistic“ algorithm „ Pessimistic“ algorithm
Conclusions ,[object Object],[object Object],[object Object],[object Object]
Future Work What model elements are insignificant? ,[object Object],[object Object],[object Object],How to abstract insignificant elements?

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On Application Of Structural Decomposition For Process Model Abstraction

  • 1. On Application of Structural Decomposition for Process Model Abstraction Artem Polyvyanyy Sergey Smirnov Mathias Weske BPSC 2009 24 March 2009
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Aggregation vs. Elimination Aggregate Eliminate
  • 7. Assumption: Sound Process Models Hidden deadlock Hidden unsafe process fragment unsound model sound model Assume initial models to be sound
  • 9. Order Preserving Abstraction F A A and B belong to F A , ordering constraints are lost C and D do not belong to F A , ordering constraints are preserved A belong to F A and D does not, ordering constraints between F and D as between A and D
  • 10.
  • 11.
  • 12. Relations between SESE Fragments p arent - child predecessor-successor if the node set of SESE fragment f 1 is the subset of node set of SESE fragment f 2 , then f 1 is the child of f 2 and f 2 is the parent of f 1 SESE fragment f 1 precedes SESE fragment f 2 (and f 2 succeeds f 1 ) if the outgoing edge of f 1 is the incoming edge of f 2 P 1 c 2 c 1 p 1 s 2 s 1 p 2
  • 13. Process Structure Tree parent-child predecessor-successor
  • 14.
  • 15.
  • 16. Abstraction Smoothness smoothness = 2 smoothness = 2 smoothness = 5 … loss of information is essential and desired … abstraction smoothness quantitatively estimates the information loss produced by one abstraction step
  • 17.
  • 18. Smoothness Evaluation (II) „ Optimistic“ algorithm „ Pessimistic“ algorithm
  • 19.
  • 20.