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Book ChapterDOI

Process equivalence: comparing two process models based on observed behavior

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TLDR
This paper proposes a completely new way of comparing process models that is able to avoid the two problems: directly comparing two models and compared with respect to some typical behavior.
Abstract
In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matching some desired service description, or to compare some normative process model with a process model discovered using process mining techniques. Although many researchers have worked on different notions of equivalence (e.g., trace equivalence, bisimulation, branching bisimulation, etc.), most of the existing notions are not very useful in this context. First of all, most equivalence notions result in a binary answer (i.e., two processes are equivalent or not). This is not very helpful, because, in real-life applications, one needs to differentiate between slightly different models and completely different models. Second, not all parts of a process model are equally important. There may be parts of the process model that are rarely activated while other parts are executed for most process instances. Clearly, these should be considered differently. To address these problems, this paper proposes a completely new way of comparing process models. Rather than directly comparing two models, the process models are compared with respect to some typical behavior. This way we are able to avoid the two problems. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics.

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Journal ArticleDOI

Similarity of business process models: Metrics and evaluation

TL;DR: Three similarity metrics that can be used to answer queries on process repositories are presented, including node matching similarity that compares the labels and attributes attached to process model elements; structural similarity that connects element labels as well as causal relations captured in the process model.
Book ChapterDOI

Graph Matching Algorithms for Business Process Model Similarity Search

TL;DR: In this paper, the problem of ranking all process models in a repository according to their similarity with respect to a given process model is investigated, and four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one, are evaluated.
Journal ArticleDOI

Mining process models with non-free-choice constructs

TL;DR: This paper proposes an algorithm that is able to deal with both kinds of causal dependencies between tasks, i.e., explicit and implicit ones, and implements it in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.
Book ChapterDOI

Measuring Similarity between Business Process Models

TL;DR: In this article, the authors use causal footprints as an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs.
Journal Article

Similarity search of business process models

TL;DR: Recent work on an instance of this class of problems, where the objects in question are business process models, is reviewed to identify process models in a repository that most closely resemble a given process model or a fragment thereof.
References
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Book

A calculus of communicating systems

Robin Milner
TL;DR: A case study in synchronization and proof techniques, and some proofs about data structures in value-communication as a model of CCS 2.0.
Journal ArticleDOI

Workflow mining: discovering process models from event logs

TL;DR: A new algorithm is presented to extract a process model from a so-called "workflow log" containing information about the workflow process as it is actually being executed and represent it in terms of a Petri net.
Book ChapterDOI

The prom framework: a new era in process mining tool support

TL;DR: The ProM framework is introduced and an overview of the plug-ins that have been developed and is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas.
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