Nodal Network Data Visualization for Efficient Analysis
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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 structure data using relational computer models, parsing data into domain and dimension tables, generating nodal networks, and displaying data through a graphical user interface, allowing users to interactively explore and analyze data by linking nodes and executing analysis protocols.
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 large volumes of data into structured data tables with hierarchical relationships (parent-child nodes). This segmentation allows the system to navigate and analyze data in manageable units rather than processing entire datasets, significantly reducing computational resource requirements while maintaining analysis productivity.
Solution Approach 2:
The system performs preliminary actions by pre-structuring data into standardized tables with defined relationships before analysis. Data is organized into hierarchical nodal networks in advance, creating a ready-to-navigate structure that eliminates the need for resource-intensive processing during actual analysis operations.
2Ease of operation
If conventional visualization tools are used to filter data, then data can be viewed, but the burden of data navigation is shifted to users
Solution Approach 1:
The system implements self-service by automatically navigating and presenting data through pre-structured tables and hierarchical relationships. The system serves itself by managing data organization and presentation without requiring users to manually filter or navigate through large datasets, thereby reducing user effort and time investment.
3Measurement precision
If conventional methods are used to analyze complex datasets, then insights can be identified, but a systematic and consistent approach is lacking
Solution Approach 1:
The patent implements a universal data table structure that can handle multiple types of data (structured and unstructured) through a consistent hierarchical framework. This multi-functional structure provides systematic and consistent data organization across different data types, enabling precise and repeatable analysis without increasing operational 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.