3D Data Visualization for Enterprise Search
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Solution Overview
Problem
Current data analysis and visualization methods for enterprise search are inefficient, as they often result in slow search processes and outputs that are difficult for users to understand, leading to reduced value from stored data and decreased efficiency due to the inability to effectively utilize massive amounts of unstructured text data.
Innovation Solution
A data analysis system comprising a CPU, Raw Pair Distance (RPD) module, Mean Pair Distance (MPD) module, Energy Reduction (ER) module, and 3D visualizer, which processes text data to create a raw pair distance table, nodes table, node-node distance matrix, and NSPACE matrix, enabling the visualization of complex data relationships in a user-friendly 3D format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional data search methods are used to process massive quantities of stored data, then the search process can be performed, but the search speed becomes slow and the output becomes difficult for users to understand
Solution Approach 1:
The patent transforms complex data relationships from traditional tabular formats into three-dimensional visual representations. The system creates 3D models where data points are positioned in spatial dimensions based on their relationships, allowing users to visually navigate and understand complex datasets through spatial patterns rather than text-heavy tables, thereby improving both search efficiency and user comprehension
Solution Approach 2:
The system creates simplified visual copies of complex data structures. Instead of presenting raw data tables directly to users, it generates 3D visual models that replicate the essential relationships and patterns in the data in an easily interpretable format, making the output both faster to process and easier to understand
2Loss of information
If traditional search methods are used to retrieve data from databases, then data can be found, but the value of stored data is reduced due to slow processing and difficult-to-understand output
Solution Approach 1:
The patent preserves complete data information while adding a spatial dimension for visualization. The 3D models maintain all original data relationships and can be interactively explored to retrieve full data details, preventing information loss while dramatically reducing the time needed to understand and navigate datasets through visual pattern recognition
3Loss of information
If complex data analysis is performed on unstructured text data, then insights can be gained, but the system efficiency decreases due to the complexity of processing and presenting the data
Solution Approach 1:
The system extracts essential relationships and patterns from large volumes of unstructured text data and represents them in condensed 3D visual forms. By separating the analysis complexity from the presentation layer, the system maintains deep analytical insights while presenting simplified visual models that improve system efficiency and user productivity
Solution Approach 2:
The patent creates visual copies of complex analytical results in 3D space. These visual representations capture the essence of complex data relationships without requiring the system to continuously process and present raw complex data, thereby maintaining analytical depth while improving processing efficiency
Data Source
AI summary
Systems and methods for analyzing structured data are described. A device may receive a table of structured data and create a Raw Pair Distance (RPD) table. The device then selects a set of nodes from the elements in the RPD table and outputs a nodes table. The device may also output a node-node distance (NND) matrix using the RPD table and run an energy reduction algorithm on the NND matrix in order to create an NSPACE matrix including n-dimensional coordinates for each node. The device may display (e.g., via a 3D visualizer) a graphical representation of selected nodes and coordinated relationships between the selected nodes. The systems and methods may enable a user to quickly search and understand relationships within a large structured data set.