A Machine Learning Approach to Workflow Management View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2000

AUTHORS

Joachim Herbst

ABSTRACT

There has recently been some interest in applying machine learning techniques to support the acquisition and adaptation of workflow models. The different learning algorithms, that have been proposed, share some restrictions, which may prevent them from being used in practice. Approaches applying techniques from grammatical inference are restricted to sequential workflows. Other algorithms allowing concurrency require unique activity nodes. This contribution shows how the basic principle of our previous approach to sequential workflow induction can be generalized, so that it is able to deal with concurrency. It does not require unique activity nodes. The presented approach uses a log-likelihood guided search in the space of workflow models, that starts with a most general workflow model containing unique activity nodes. Two split operators are available for specialization. More... »

PAGES

183-194

Book

TITLE

Machine Learning: ECML 2000

ISBN

978-3-540-67602-7
978-3-540-45164-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45164-1_19

DOI

http://dx.doi.org/10.1007/3-540-45164-1_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019466628


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