The state-of-art approaches in structural similarities
of process models base their operations on behavioral
data and text semantics. These data is usually missing from mock-up or obfuscated process models. This fact makes it complicated to apply current approaches on these types of models. We examine the problem of the automated detection of re-occurring structures in a collection of process models, when text semantics or behavioral data are missing. This problem
is a case of (sub)graph isomorphism, which is mentioned as NP-complete in the literature. Since the process models are very special types of attributed directed graphs we are able to develop an approach that runs with logarithmic complexity. In this work we set the theoretical basis, develop a configurable approach for the detection of re-occurring structures in any process models collection, and validate it against a set of BPMN 2.0 models. We define two execution scenarios and discuss the relation of the execution times with the complexity of the comparisons. Finally, we analyze the detected structures, and
propose the configurations that lead to optimal results.
1. University of Stuttgart
Universitätsstr. 38
70569 Stuttgart
Germany
Phone +49-711-685 88477
Fax +49-711-685 88472
Research
Marigianna Skouradaki, Katharina Görlach,
Michael Hahn, Frank Leymann
Institute of Architecture of Application Systems
{firstname.lastname}@iaas.uni-stuttgart.de
Applying Subgraph Isomorphism
to Extract Reoccurring Structures
from BPMN 2.0 Process Models