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)
- High, simple extensibility 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: visualize data and results through several interactive views, allowing for 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
Check out the complete node documention for a comprehensive list of nodes are detailed descriptions.
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 - 64bit Intel-based architecture with Java 1.6