Scientific Workflow Management with ADAMS
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.
Cite
Text
Reutemann and Vanschoren. "Scientific Workflow Management with ADAMS." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_58Markdown
[Reutemann and Vanschoren. "Scientific Workflow Management with ADAMS." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/reutemann2012ecmlpkdd-scientific/) doi:10.1007/978-3-642-33486-3_58BibTeX
@inproceedings{reutemann2012ecmlpkdd-scientific,
title = {{Scientific Workflow Management with ADAMS}},
author = {Reutemann, Peter and Vanschoren, Joaquin},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {833-837},
doi = {10.1007/978-3-642-33486-3_58},
url = {https://mlanthology.org/ecmlpkdd/2012/reutemann2012ecmlpkdd-scientific/}
}