Description:

ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.

Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators.

Operators include machine learning (WEKA, MOA, MEKA, deeplearning4j) and image processing (ImageJ, JAI, BoofCV, OpenImaJ,LIRE, ImageMagick and Gnuplot). R available using Rserve. WEKA webservice allows other frameworks to use WEKA models. Fast prototyping with Groovy and Jython. Read/write support for various databases and spreadsheet applications.

Changes to previous version:

Some highlights:

• Code base was moved to Github
• Nearly 90 new actors, 25 new conversions
• much improved deeplearning4j module
• experimental support for Microsoft's CNTK deep learning framework
• rsync module
• MEKA webservice module
• improved support for image annotations
• improved LaTeX support
• Websocket support

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Csv, Tab Separated, Libsvm, Xrff, Excel, Odf, Xls, Xlsx

Tags: R, Workflow, Weka, Image Processing, Webservice, Moa, Gis, Openstreetmap, Meka

Archive: download here

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