Scientific workflow management with ADAMS

Authors:
Peter Reutemann;Joaquin Vanschoren
Affiliations:
University of Waikato, Hamilton, New Zealand;Leiden University, Leiden, Netherlands
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Year:
2012

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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.