Nodal Network Data Visualization for Complex Structured Datasets
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Solution Overview
Problem
Conventional data retrieval and visualization methods are inefficient for navigating large volumes of 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 and system that structures data using relational computer models, parsing data into domain and dimension tables to generate nodal networks, allowing for efficient navigation and visualization through a graphical user interface, with nodes representing data and metadata linking related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data analysis methods are used to navigate large volumes of data, then data can be analyzed, but high processing power and computing resources are required
Solution Approach 1:
The patent segments data into structured data tables with specific schemas and nodal networks with defined nodes and relationships. This segmentation organizes data into manageable units that can be processed more efficiently, reducing the computational resources required for navigation and analysis while maintaining productivity.
2Ease of operation
If conventional visualization tools are used, then data can be displayed, but users must define various thresholds and filters which shifts the burden of data navigation to users
Solution Approach 1:
The patent implements self-service through automated tag generation and hierarchical organization of data. The system automatically creates tags, defines relationships, and structures data without requiring users to manually define thresholds and filters. This reduces the operational burden on users while the systematic structure provides a consistent visualization approach.
3Adaptability or versatility
If conventional methods are used to manage data 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 data structure through standardized data tables and nodal networks that can represent various types of data (structured and unstructured) and their relationships. This universal framework enables consistent data management across different platforms, handling diverse data types and relationships without increasing management complexity.
Data Source
AI summary
Disclosed methods and systems describe an analytics server that generates an inter-related nodal data structure. The analytics server receives an electronic template having a set of input fields, the electronic template identifying at least a portion of data stored within a database and its corresponding domain data table and a display attribute, the electronic template further identifying a database storing the data; retrieves the data from the database; parses the data into a set of unique domain data tables having a first criterion and a set of unique dimension tables having a second criterion; generates a nodal network comprising a set of nodes where each node represents at least a portion of the retrieved data, each node having metadata comprising a unique identifier corresponding to a unique domain table and a unique dimension table corresponding to data associated with each node; links one or more nodes based their respective metadata.