New Features with KNIME Analytics Platform
New Features with KNIME Analytics Platform : KNIME is the leading open platform for data-driven innovation helping organizations to stay ahead of change to discover the potential hidden in your data, mine for fresh insights or predict new futures. Its code-free setup and intuitive interface mean get to spend more quality time with data and set up a collaborative platform for joint development, analytics and effortless sharing of knowledge, tools and insights with minimal investment. KNIME also provides the ability to develop reports based on your information or automate the application of new insight back into production systems. KNIME Analytics Platform is open source and available under GPL license. It can be extended with KNIME Commercial Software to include professional support, productivity and collaboration functionality, providing the best of both worlds.
The KNIME Analytics Platform incorporates hundreds of processing nodes for data I/O, preprocessing and cleansing, modeling, analysis and data mining as well as various interactive views, such as scatter plots, parallel coordinates and others. It integrates all of the analysis modules of the well known Weka data mining environment and additional plugins allow R-scripts to be run, offering access to a vast library of statistical routines. KNIME is based on the Eclipse platform and, through its modular API, easily extensible.
New Decision Tree to Rule Set node converts (a single) Decision Tree model to a PMML-formatted Rule Set model and also to a data table containing the rules in a textual form. The Rule Engine (Dictionary) applies rules from a rule table to a data table. The Missing Value node now exports a PMML model and can feed a Missing Value (Apply) node. It also supports more options such as "previous" or "linear interpolation". Another new node allows columns to be filtered that have more than a specified fraction of missing values.
There are two new nodes for numerical data generation under IO/Other/Modular Data Generation. Visual 2D Data Generator creates a two dimensional data set by manually adding/clicking points in a coordinate system. Quasi-Random Sequence Generator (Apache) generates a quasi-random sequence to cover all parts of a high dimensional space. In KNIME Labs, there is a new category that is dedicated to interacting with MongoDB. A number of nodes is available in order to read, remove, save, update, and write data from and to a MongoDB database.
The KNIME Server now implements a REST interface. This means that a workflow can be exported as a REST service. KNIME partner Actian has made available freely the DataFlow Streaming Execution engine to speed up execution of KNIME nodes on a parallel architecture.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.