Structured Data Tables and Nodal Networks for Large-Scale Analysis
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Solution Overview
Problem
Conventional data analysis methods face challenges in efficiently navigating and visualizing large volumes of structured and unstructured data due to high processing power requirements and lack of systematic approaches, leading to inefficient decision-making and data navigation burden on users.
Innovation Solution
A method involving parsing data into domain and dimension tables, generating nodal networks with metadata-linked nodes, and displaying data on a graphical user interface, allowing interactive visualization and clustering of data based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used to analyze large volumes of data, then data insights can be obtained, 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 processing power required while maintaining analysis capability.
Solution Approach 2:
The patent introduces structured data tables and nodal networks as intermediary structures between raw data and analysis operations. These intermediaries organize data in advance with predefined schemas and relationships, enabling efficient querying and analysis without requiring high processing power during actual analysis operations.
2Ease of operation
If conventional visualization tools are used to navigate large volumes of data, then data can be displayed, but users bear the burden of data navigation and systematic approaches are lacking
Solution Approach 1:
The patent performs preliminary organization of data into structured tables and nodal networks with predefined schemas, relationships, and hierarchies before users need to access the data. This preliminary structuring eliminates the need for users to perform complex navigation tasks, as the data is already organized in an easily accessible format with clear pathways for exploration.
Solution Approach 2:
The structured data tables and nodal networks are designed to be self-descriptive with embedded schemas, data types, and relationship definitions. This allows the system to automatically guide users through data exploration without requiring users to understand complex navigation procedures or systematically approach data analysis.
3Loss of information
If existing online tools are used to identify insights from large data volumes, then insights can be obtained, but managing information across different platforms is difficult
Solution Approach 1:
The patent merges data from multiple sources and platforms into unified structured data tables and integrated nodal networks. By consolidating information into a single standardized structure with consistent schemas and relationships, the system eliminates the complexity of managing information across different platforms while preserving all insights from the source data.
Solution Approach 2:
The structured data table format and nodal network structure are designed as universal containers that can hold and organize data from any source or platform. This multi-functional structure allows the same system to manage diverse information types and sources without requiring platform-specific management approaches.
Data Source
AI summary
Disclosed are methods and computer systems to generate, update, traverse, and analyze a nodal data structure based on data associated with an entity. The methods and systems disclosed herein describe a server that can generate and link various nodes in a nodal network and parse data into unique domain tables. When the server receives a request to analyze the data, the server executes clustering algorithms to identify preferred nodes that correspond to one or more attributes within the received request. The server then executes one or more analytical protocols using the preferred nodes and displays the results.