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dc.contributor.authorReutemann, Peter
dc.contributor.authorVanschoren, Joaquin
dc.date.accessioned2012-10-30T03:47:30Z
dc.date.available2012-10-30T03:47:30Z
dc.date.copyright2012
dc.date.issued2012
dc.identifier.citationReutemann, P. & Vanschoren, J. (2012). Scientific workflow management with ADAMS. In Lecture Notes in Computer Science, 2012, Volume 7524 LNAI, Issue Part 2: Machine Learning and Knowledge Discovery in Databases,pp. 833-837.en_NZ
dc.identifier.isbn9783642334856
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/10289/6766
dc.description.abstractWe demonstrate the Advanced Data mining And Machine learning System (ADAMS), a novel workflow engine designed for rapid prototyping and maintenance of complex knowledge workflows. ADAMS does not require the user to manually connect inputs to outputs on a large canvas. It uses a compact workflow representation, control operators, and a simple interface between operators, allowing them to be auto-connected. It contains an extensive library of operators for various types of analysis, and a convenient plug-in architecture to easily add new ones.en_NZ
dc.language.isoen
dc.publisherSpringeren_NZ
dc.subjectdata miningen_NZ
dc.subjectmachine learningen_NZ
dc.subjectscientific workflowsen_NZ
dc.titleScientific workflow management with ADAMSen_NZ
dc.typeChapter in Booken_NZ
dc.identifier.doi10.1007/978-3-642-33486-3_58en_NZ


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