Digital Duplicate Semantic Navigation for Flexible Data Access
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Solution Overview
Problem
Conventional data storage and access systems are limited in scope, flexibility, and integration, requiring predefined schemas and prior knowledge of data architecture for query outputs, and struggle with horizontal expansion across multiple tables.
Innovation Solution
A digital duplicate data structure utilizing a dynamic model with neuro-synaptic data relationships allows for efficient data ingestion, access, and adaptation to organizational changes without redesign, enabling flexible data synthesis and association without prior knowledge of the data storage architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data storage systems with predefined schemas are used, then data can be stored and accessed, but the systems lack flexibility and require prior knowledge of data architecture for query outputs
Solution Approach 1:
The patent implements a dynamic data structure where entity relationships are not fixed but can be modified at runtime. The system allows users to create, modify, and delete relationships between data entities without requiring changes to the underlying schema or data architecture, enabling flexible adaptation to organizational changes while maintaining simple access patterns
Solution Approach 2:
The patent introduces an intermediary layer (the dynamic relationship model) between the underlying data storage and the user interface. This intermediary allows users to interact with data through flexible, high-level relationships without exposing the complexity of the underlying data architecture, thereby providing both flexibility and simplicity
2Adaptability or versatility
If conventional data storage systems are used, then data can be stored, but horizontal expansion across multiple tables is limited
Solution Approach 1:
The patent creates a universal relationship model that can connect any data entity to any other entity across multiple tables and data sources. This universal linking capability enables horizontal expansion of the data model without requiring complex, custom integration logic for each new connection, as the same relationship mechanisms apply throughout the entire system
3Productivity
If predefined schema systems are used, then data storage is efficient, but the systems require redesign when organizational changes occur
Solution Approach 1:
The system maintains efficient data storage through its structured relationship model while simultaneously allowing dynamic modification of entity relationships. When organizational changes occur, users can update relationships without triggering system-wide redesign, thus preserving both storage efficiency and adaptability
4Ease of operation
If conventional query systems are used, then data can be retrieved, but users must have prior knowledge of the data storage architecture
Solution Approach 1:
The patent introduces an intermediary relationship model that sits between the user and the underlying data architecture. This intermediary allows users to query and manipulate data through intuitive, high-level relationships without needing to understand the complex data storage architecture, thereby improving ease of operation while hiding architectural complexity
Data Source
AI summary
Disclosed herein is a software tool that may be used to create and navigate through visualizations of an organization's data as embodied in a digital duplicate. These “smart interactions” may allow a user to visually and intuitively traverse the semantic network to dynamically provide on-demand visualization of the business data. Based on the traversal of the network, the network can be simplified and/or subnetworks can be created. As part of the visualization, “perspectives” can be created based on focal points (i.e., nodes) in the network. The focal points may be used as a starting point for the traversal, and functions (e.g., aggregates) can be dynamically applied to produce subnetworks to provide data for visualizations.