Description:

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. GIS support is possible with the OpenStreetMap integration.

Changes to previous version:

• 51 new actors
• 16 new conversions
• new module adams-jooq: code generation from JDBC databases for typed access
• new module adams-image-webservice: allows upload of images using webservice
• adams-timeseries module extended
• adams-spreadsheet module extended
• adams-random module extended
• adams-imaging module overhaul

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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