Navianalytics allows the combination of structured, unstructured, complete, incomplete, dispersion, no dispersion, normal, non-normal or hybrid data within memory resistor or two-terminal but non-volatile memory architecture.  The leveraging of memory resistor allows for more data storage capabilities which are also one hundred times faster than contemporaneous flash memory while at the same time, there is the use of less energy.

 

The integrative services processor auto-integrates data sources (Big and Small data) into an ontology-based data integration. There is the automated data mining and the efficient automatic search query formulation which can leverage phrase level analysis

 

The Machine Learned Analytics Engines leverage modeling techniques that provide solutions such as as segmentation models,  predictive models and optimization models. The models generated can be moved to in-memory holding which can then be overwritten with more stable or more robust models based on the increasing quality of the machine learned data.This is constantly an on-going process.

 

As new variables become more available through the auto-refactoring processing, the model executor, model calibrator and model director step in to produce models that can be optimized based on the requestor's attempting to leverage the models through the machine learned intelligence flows that are constantly self healing with auto recalculating and auto recalibrating capabilities.

 

So from high level, welcome to Virtual Prescriptive Analytics which allows the business leaders to actually touch the realm of impossible to generate, rationalize and modify performance outlook that benefits the stakeholders and the shareholders.