Project details for ADAMS

Screenshot ADAMS 0.4.2

by fracpete - February 26, 2013, 03:26:25 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. Via Rserve, the R-Project can be incorporated in flows for data processing. With the WEKA webservice, other frameworks can take advantage of WEKA's models as well. For fast prototyping the user can use scripting languages such as Groovy and Jython.

Changes to previous version:
  • Added almost 20 more conversions and 20 new actors
  • R-Project integration using Rserve
  • WEKA webservice allows for programming language agnostic training, evaluation and use of WEKA models (classifiers, clusterers) and data processing using filters
  • Spreadsheets now come with basic formula support
  • Spreadsheets can be used for lookup tables in the flow
  • Support for "chunked" reading/writing of spreadsheets to process millions of rows
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
Archive: download here


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