One key behind the success of KNIME is its inherent modular workflow approach, which documents and stores the analysis process in the order it was conceived and implemented, while ensuring that intermediate results are always available.

Core KNIME features include:

  • Scalability through sophisticated data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
  • Highly and easily extensible via a well-defined API for plugin extensions
  • Intuitive user interface
  • Import/export of workflows (for exchanging with other KNIME users)
  • Parallel execution on multi-core systems
  • Command line version for "headless" batch executions

Available KNIME modules cover a vast range of functionality, such as:

  • I/O: retrieves data from files or data bases
  • Data Manipulation: pre-processes your input data with filtering, group-by, pivoting, binning, normalization, aggregation, joining, sampling, partitioning, etc.
  • Views: inspects the data and results with several interactive views, supporting interactive data exploration
  • Hiliting: ensures hilited data points in one view are also immediately hilited in all other views
  • Mining: uses state-of-the-art data mining algorithms like clustering, rule induction, decision tree, association rules, naïve bayes, neural networks, support vector machines, etc. to better understand your data

Supported Operating Systems

  • Windows - 32bit (regularly tested on XP and Vista)
  • Windows - 64bit (regularly tested on Vista and verified to work under Windows 7)
  • Linux - 32bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
  • Linux - 64bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
  • Mac OSX - with KNIME 2.1 we are also able to release a preliminary, not fully tested, experimental KNIME version for Mac OSX which requires a 64bit Intel-based architecture with Java 1.6

List of Available Nodes (Modules)


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