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. Motivation Research project with AOK Brandenburg Goal: detailed process models abstract process models * example model : > 300 nodes >150 functions graph-structured model ≈ 4 000 EPCs graph-structured process models
- 3. Business Process Model Abstraction … is generalization of a model, leaving out insignificant process details in order to reduce model complexity and retain information relevant for a particular purpose.
- 4. Business Process Model Abstraction What model elements are insignificant? out of scope possible criteria: non-functional properties semantics SESE decomposition of a model abstraction mechanism How to abstract insignificant elements?
- 5. Process Model (N, E, type) is a business process model, where: is a set of nodes, where N A ≠ Ø – a set of activities; N G – a set of gateways; the sets are disjoint is a set of directed edges between nodes representing control flow is a connected graph every activity has at most 1 incoming & at most 1 outgoing edge there is at least 1 activity with no incoming edges (start activity) and at least 1 activity with no outgoing edges (end activity) assigns control flow construct to a gateway every gateway is either a split or a join; splits have exactly 1 incoming edge and at least 2 outgoing; joins have at least 2 incoming edges and exactly 1 outgoing.
- 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. Single Entry Single Exit Fragment SESE fragment is a fragment which has exactly: 1 incoming edge 1 outgoing edge
- 11. Canonical SESE Fragment canonical SESE fragments non-canonical SESE fragments
- 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. Auxiliary Concepts A – an activity to be abstracted sese A – canonical SESE fragment containing A (is a leaf in the PST) sese min – a minimal canonical SESE fragment containing A and at least one more activity ( sese min ≠ sese A ); there are 2 options for sese min : there is canonical sese fragment sese A’ which is in predecessor-successor relation with sese A ; then sese min is a SESE fragment with the incoming edge of the predecessor and the outgoing edge of the successor if 1 does not hold, than sese min is a SESE fragment which is the parent of sese A
- 15. Abstraction Algorithm define the set of activities to be abstracted (let it be I A ); if I A has elements, select one activity from the set (let it be A ); else go to 8 ; find sese min for A; remove from I A all the activities which belong to sese min ; replace sese min with aggregating activity with the incoming edge of sese min and the outgoing edge of sese min ; if necessary, add the new aggregating activity to I A ; go to 2 ; stop.
- 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. Smoothness Evaluation (I) Experiment with process models: 50 models real world process models 50 <|N| < 205 graph-structured models
- 18. Smoothness Evaluation (II) „ Optimistic“ algorithm „ Pessimistic“ algorithm
- 19. Conclusions We proposed the structural abstraction approach based on PST, which is: order preserving handles graph-structured models We evaluated the approach regarding smoothness
- 20. Future Work What model elements are insignificant? semantics of model elements more fine-grained decomposition methods prototypical implementation How to abstract insignificant elements?