Nodal Network Data Visualization for Complex Analytics
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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 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 data into structured data tables with specific schemas and nodal networks with defined nodes and relationships. This segmentation organizes large volumes of data into manageable, pre-structured units that can be queried and analyzed more efficiently, reducing the computational resources needed for data processing while maintaining analytical 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 no systematic approach is provided
Solution Approach 1:
The patent applies preliminary action by pre-structuring data into organized tables with defined schemas and creating nodal networks with established relationships before analysis. This pre-organization eliminates the need for users to manually navigate and filter through unstructured data, as the systematic structure is already in place to guide efficient data retrieval and analysis.
3Quantity of substance
If data is stored in conventional formats to maintain data volume and variety, then all data can be retained, but efficient navigation and comprehension are delayed
Solution Approach 1:
The patent transitions data from conventional flat storage formats to a multi-dimensional structure consisting of structured data tables with schemas and nodal networks with relationships. This dimensional transformation organizes data in a hierarchical and relational framework that enables efficient navigation and comprehension while preserving the full volume and variety of the original data set.
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.