Project details for ADAMS

Screenshot ADAMS 0.4.1

by fracpete - December 20, 2012, 00:46:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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The Advanced Data mining and Machine learning System, or short ADAMS, is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how the data is being processed, e.g., sequentially or in parallel. This approach allows the rapid development and easy maintenance of large workflows, consisting of hundreds or even thousands of operators. ADAMS offers operators for machine learning libraries like WEKA and MOA and image processing libraries such as ImageJ, Java Advanced Imaging (JAI), ImageMagick and Gnuplot. For fast prototyping the user can use scripting languages such as Groovy and Jython as well.

Changes to previous version:
  • Added 40 new actors, resulting in almost 400 actors for a wide range of domains available now.
  • Doubled the number of conversion schemes to almost 80, available through the Convert transformer.
  • New module for heatmap support available.
  • Much improved spreadsheet support: import/export from/to databases, improved memory efficiency, readers for WEKA file formats, filtering of rows/columns possible.
  • Built-in sqlite database support.
  • Comes now with attribute selection capability (WEKA).
  • Movie generation using the FFmpeg sink.
  • Flow editor offers a temporary clipboard for storing an arbitrary number of flow fragments, allows user to temporarily bookmark actors.
  • Updated and extended the documentation for the modules.
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Arff, Csv, Tab Separated, Libsvm, Xrff, Excel, Odf
Tags: Workflow, Weka, Image Processing
Archive: download here


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