Digital Duplicate Data Model for Semantic Query 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 user knowledge of data architecture, and struggle with horizontal expansion across multiple tables.
Innovation Solution
A digital duplicate data structure utilizing a dynamic model with semantic and structural contexts, allowing for dynamic entity relationships and automatic association formation without prior knowledge of data storage architecture, enabling efficient data ingestion, access, and adaptation to organizational changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data storage systems are built around specific business functions with predefined schemas, then data storage and access are structured and organized, but the systems lack flexibility and adaptability to organizational changes
Solution Approach 1:
The patent implements dynamic entity relationships where associations between data entities are not fixed but can be created, modified, and deleted at runtime through a drag-and-drop interface. This allows the data model to adapt to organizational changes without requiring system redesign, as relationships evolve dynamically based on user needs rather than being constrained by predefined schemas.
Solution Approach 2:
The system provides a universal data access layer that works across different business functions and data sources. The dynamic entity relationship model serves multiple purposes: storing hierarchical data, defining associations, enabling queries, and supporting analytics, replacing the need for separate specialized systems for each business function.
2Ease of operation
If conventional data systems require predefined schemas and dimensions, then data organization is structured and consistent, but user access requires a priori knowledge of data architecture
Solution Approach 1:
The system enables end users to define and modify their own data queries and relationships without requiring knowledge of the underlying data architecture. Users can drag and drop entities to create associations and build queries intuitively, making the system self-service capable and eliminating the need for users to understand complex data schemas or join operations.
Solution Approach 2:
The patent introduces an intermediary layer between the user and the data storage system. This layer translates intuitive drag-and-drop user actions into complex database operations automatically, shielding users from data architecture complexity while maintaining access to structured data organization benefits.
3Quantity of substance
If conventional relational databases are used for vertical scaling, then data capacity increases, but horizontal linking and expansion across multiple tables becomes limited
Solution Approach 1:
The patent moves from traditional two-dimensional relational tables to a multi-dimensional hierarchical data model where entities can have multiple parent and child relationships simultaneously. This enables horizontal expansion across multiple data domains without the complexity of numerous table joins, as the hierarchical structure naturally represents complex relationships in a more direct manner.
Solution Approach 2:
The system segments data into discrete entities with defined hierarchical relationships, allowing independent scaling and management of different data domains. Each entity can be manipulated, queried, and scaled independently while maintaining relationships through the dynamic entity model, reducing the complexity of horizontal linking compared to traditional relational approaches.
Data Source
AI summary
Disclosed herein is new approach for structuring an organization's data, involving at a high level establishing a digital context and populating the digital context with digital content to thereby form what is referred to herein as a digital duplicate. In one aspect, the disclosed approach may be embodied in a computer-implemented method that involves: establishing a data structure comprising (i) a structural context that has at least one data component, where each component of the structural context has associated therewith one or more respective data properties (ii) a semantic context that has at least two data types that further describe individual data properties and; and populating underlying data into an instance of the data structure such that underlying data populated into each respective property of the at least one data component has each of the at least two data types of the semantic context.