Project details for ADAMS

Screenshot ADAMS 0.4.3

by fracpete - June 10, 2013, 07:14:57 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 more than 30 new actors
  • spreadsheet support extended: merging of spreadsheets, generic object cells, multiple sheet support for reading/writing (Excel, ODF), Gnumeric reader, SQL dump reader, WEKA data formats (read/write)
  • basic JSON processing support
  • email support extended: read/write of files, viewing, basic address book
  • extended WEKA Explorer allows saving of sessions, multiple Explorer panels in same window
  • support for WEKA cluster evaluation added
  • variables can be attached to arrays now
  • many UI improvements in flow editor
  • connection standalons can prompt for password now (eg Database, SMTP, FTP, SSH)
  • enhanced image support: EXIF/IPTC meta-data, Draw actor allows basic draw operations on images
  • JavaExec actor allows launching of new JVMs with same or enhanced classpath
  • Twitter integration fixed
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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