Big Data with ADAMS

Peter Reutemann, Geoff Holmes
Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 41:5-8, 2015.

Abstract

ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython).

Cite this Paper

BibTeX
@InProceedings{pmlr-v41-reutemann15, title = {{Big Data with ADAMS}}, author = {Reutemann, Peter and Holmes, Geoff}, booktitle = {Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications}, pages = {5--8}, year = {2015}, editor = {Fan, Wei and Bifet, Albert and Yang, Qiang and Yu, Philip S.}, volume = {41}, series = {Proceedings of Machine Learning Research}, month = {10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v41/reutemann15.pdf}, url = {https://proceedings.mlr.press/v41/reutemann15.html}, abstract = {ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython).} }
Endnote
%0 Conference Paper %T Big Data with ADAMS %A Peter Reutemann %A Geoff Holmes %B Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications %C Proceedings of Machine Learning Research %D 2015 %E Wei Fan %E Albert Bifet %E Qiang Yang %E Philip S. Yu %F pmlr-v41-reutemann15 %I PMLR %P 5--8 %U https://proceedings.mlr.press/v41/reutemann15.html %V 41 %X ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython).
RIS
TY - CPAPER TI - Big Data with ADAMS AU - Peter Reutemann AU - Geoff Holmes BT - Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications DA - 2015/08/31 ED - Wei Fan ED - Albert Bifet ED - Qiang Yang ED - Philip S. Yu ID - pmlr-v41-reutemann15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 41 SP - 5 EP - 8 L1 - http://proceedings.mlr.press/v41/reutemann15.pdf UR - https://proceedings.mlr.press/v41/reutemann15.html AB - ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython). ER -
APA
Reutemann, P. & Holmes, G.. (2015). Big Data with ADAMS. Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, in Proceedings of Machine Learning Research 41:5-8 Available from https://proceedings.mlr.press/v41/reutemann15.html.

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