A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections
Becker Jörg, Breuker Dominic, Delfmann Patrick, Dietrich Hanns-Alexander, Steinhorst Matthias
Pattern detection serves different purposes in managing large collections of process models, ranging from syntax checking to compliance validation. This paper presents a runtime analysis of four graph-theoretical algorithms for (frequent) pattern detection. We apply these algorithms to large collections of process and data models to demonstrate that, despite their theoretical intractability, they are able to return results within (milli-) seconds. We discuss the relative performance of these algorithms and their applicability in practice.
Conceptual Model Analysis, Subgraph Isomorphism, Frequent Subgraph Detection, Pattern Matching