3D Text Data Visualization via NSPACE Matrix
Find Innovative SolutionsGenerate Solutions
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 datasets.
Innovation Solution
A data analysis system comprising a CPU, Raw Pair Distance (RPD) module, Mean Pair Distance (MPD) module, Energy Reduction 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 database search methods are used, then data storage capacity is maintained, but search speed and user comprehension are reduced
Solution Approach 1:
The patent segments the complex search process into distinct modules: RPD module for calculating raw pair distances between terms, MPD module for computing mean pair distances and selecting nodes, Energy Reduction module for optimizing node positions, and 3D visualizer for rendering. This segmentation allows each module to handle specific computational tasks efficiently, improving overall search speed while managing complexity through modular design.
Solution Approach 2:
The patent transforms traditional flat search results into three-dimensional visual representations. By mapping terms and their relationships into 3D space based on calculated distances, the system provides intuitive spatial visualization that enhances user comprehension without sacrificing search speed, as the dimensional transformation is performed through efficient algorithmic processing.
2Measurement precision
If detailed text analysis is performed on large datasets, then insight quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating pair distances between all terms in the corpus and storing them in structured formats (raw pair distance tables, node-node distance matrices). This preprocessing step enables rapid retrieval and analysis during actual search operations, maintaining high analysis precision while reducing real-time processing time through efficient query execution on pre-processed data.
Data Source
AI summary
Systems and methods for analyzing a large number of textual passages are described. A computing device receives the textual passages as input and generates a Raw Pair Distance (RPD) table. The device then determines a Node table and an Node-Node Distance (NND) matrix from the RPD table. An energy reduction process is used to generate an NSPACE matrix from the NND matrix. Finally, a 3D visualizer displays aspects of the Nodes table and the NSPACE matrix to a user. The systems and methods may enable a user to quickly search and understand the text relationships within the large number of textual passages.