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https://eprints.qut.edu.au/291...
Part of book or chapter of book
Data sources: UnpayWall
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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2010 . Peer-reviewed
License: Springer TDM
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Merging Business Process Models

Authors: Marcello La Rosa; Marlon Dumas; Reina Uba; Remco M. Dijkman;

Merging Business Process Models

Abstract

This paper addresses the following problem: given two business process models, create a process model that is the union of the process models given as input. In other words, the behavior of the produced process model should encompass that of the input models. The paper describes an algorithm that produces a single configurable process model from a pair of process models. The algorithm works by extracting the common parts of the input process models, creating a single copy of them, and appending the differences as branches of configurable connectors. This way, the merged process model is kept as small as possible, while still capturing all the behavior of the input models. Moreover, analysts are able to trace back which model(s) a given element in the merged model originates from. The algorithm has been prototyped and tested against process models taken from several application domains.

Countries
Netherlands, Australia
Keywords

Process Model, Merge, Configuration

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
59
Top 10%
Top 10%
Top 1%
Green