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.

Changes to previous version:

• Added 30 more conversions and 70 more actors
• new timeseries module, includes Weka's Forecasting plugin
• OCR support using TesseractOCR wrapper
• extended JSON support (value extraction using JSON path)
• support for processing XML/HTML (DOM generation, XSLT, XPath)
• SQL-like query language for spreadsheets
• generic support Java properties files (read/write/modify)
• generic serialization support
• support for sequence plotter overlays
• basic WebServer capability (using Jetty)
• CSV file reader/writer now support file encodings (eg UTF-8, UTF-16)

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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