Nodal Network Data Visualization for Complex Structured Tables
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Solution Overview
Problem
Conventional data retrieval and visualization methods are inefficient for large volumes of structured, semi-structured, and unstructured data, requiring high processing power and shifting the burden of data navigation to users, and lacking a systematic approach to visualize complex data sets.
Innovation Solution
A method involving parsing data into domain and dimension tables, generating nodal networks where each node represents data with metadata, linking nodes based on metadata, and displaying authorized data subsets to users through a graphical user interface, with an analytical protocol executed upon request, allowing for efficient data navigation and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data analysis methods are used to analyze large volumes of data, then data insights can be identified, but high processing power and computing resources are required
Solution Approach 1:
The patent segments large volumes of data into structured data tables with specific schemas and nodal networks with defined nodes and relationships. This segmentation allows the system to process and analyze data in manageable units rather than handling entire large datasets at once, reducing the computational resources required while maintaining analysis capability.
2Ease of operation
If conventional visualization tools are used to navigate large volumes of data, then data can be filtered using thresholds, but the burden of data navigation is shifted to users and the approach is inefficient
Solution Approach 1:
The patent introduces structured data tables and nodal networks as intermediary layers between the raw data and the user interface. These intermediaries automatically organize and structure the data, eliminating the need for users to manually filter and navigate through large datasets using thresholds. The system handles the navigation burden, improving both ease of operation and efficiency.
3Adaptability or versatility
If conventional methods are used to manage information on different platforms, then data can be stored, but managing information is difficult due to number, size, content, or relationships of the structured and/or unstructured data
Solution Approach 1:
The patent creates a universal structured data table format and nodal network structure that can accommodate different types of data (structured and unstructured) across multiple platforms. This universal structure handles variations in number, size, content, and relationships of data through standardized schemas and node definitions, reducing management complexity while maintaining adaptability.
Data Source
AI summary
Methods and systems described herein allow a server to generate a nodal data structure by parsing data into a set of domain data tables, each domain data table corresponding to a domain having a first criterion, parsing each domain data table into a set of dimension data tables, each dimension data table corresponding to a dimension having a second criterion, and generating a nodal network comprising a set of nodes where each node represents at least a portion of the data, each node having metadata comprising an identifier corresponding to a particular domain data table and a particular dimension table corresponding to data associated with each node. Upon receiving a request, the server parses the request to identify a first subset of the set of nodes associated with the request and identifies a second subset of the set of nodes authorized to be accessed by the user computing device. The server then displays data associated with the second subset of the set of nodes.