Scientific workflow management with ADAMS
dc.contributor.author | Reutemann, Peter | |
dc.contributor.author | Vanschoren, Joaquin | |
dc.date.accessioned | 2012-10-30T03:47:30Z | |
dc.date.available | 2012-10-30T03:47:30Z | |
dc.date.copyright | 2012 | |
dc.date.issued | 2012 | |
dc.description.abstract | We 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.identifier.citation | Reutemann, 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.doi | 10.1007/978-3-642-33486-3_58 | en_NZ |
dc.identifier.isbn | 9783642334856 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://hdl.handle.net/10289/6766 | |
dc.language.iso | en | |
dc.publisher | Springer | en_NZ |
dc.subject | data mining | en_NZ |
dc.subject | machine learning | en_NZ |
dc.subject | scientific workflows | en_NZ |
dc.title | Scientific workflow management with ADAMS | en_NZ |
dc.type | Chapter in Book | en_NZ |
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