Digital Duplicate Data Onboarding With Semantic Network Mapping
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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, and struggle with horizontal expansion across multiple tables.
Innovation Solution
A digital duplicate data structure using a dynamic model with neuro-synaptic principles allows for dynamic entity relationships, enabling efficient data ingestion, access, and adaptation to organizational changes without redesigning the data system, and establishes relationships through semantic networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data storage systems use predefined schemas and fixed relationships, then data integrity and consistency are maintained, but flexibility and adaptability to organizational changes deteriorate
Solution Approach 1:
The patent implements dynamic entity relationships where associations between data entities can be modified, added, or removed without redesigning the entire data system. The digital duplicate structure allows relationships to evolve with organizational changes while maintaining data integrity through the underlying graph database consistency mechanisms.
Solution Approach 2:
The system allows changing data model parameters such as entity attributes and relationship types without requiring schema migrations. The dynamic data model enables parameter modifications at runtime, allowing the system to adapt to new requirements while preserving existing data relationships.
2Measurement precision
If conventional systems require prior knowledge of data architecture for data access, then data retrieval accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces a digital duplicate as an intermediary layer between users and the underlying data architecture. This digital twin abstracts complex data relationships and provides simplified access interfaces, allowing users to query data without needing to understand the underlying graph database structure while maintaining accurate data retrieval.
Solution Approach 2:
The system creates a digital duplicate (copy) of the organizational data that mirrors the structure and relationships. This copy can be queried and manipulated independently, providing accurate data access without requiring users to understand the original data architecture's complexity.
3Ease of manufacture
If conventional data systems are built around specific business functions, then functional specialization is improved, but integration capability and scope deteriorate
Solution Approach 1:
The patent implements a universal data model using graph database entities and relationships that can represent multiple business functions within a single integrated system. The digital duplicate structure allows the same data framework to serve CRM, supply chain, manufacturing, and other business functions simultaneously, enabling both specialization and integration.
Solution Approach 2:
The system segments business functions into modular entity types and relationships within the graph database, allowing each business function to be represented as distinct but interconnected entities. This segmentation enables functional specialization while the underlying graph structure provides integration capabilities across all functions.
4Quantity of substance
If conventional relational databases use vertical scaling, then data depth and record volume are improved, but horizontal linking and expansion capability deteriorate
Solution Approach 1:
The patent transitions from the vertical scaling paradigm of relational databases to the horizontal expansion capability of graph databases. The digital duplicate structure uses graph theory to enable relationships and connections across data entities in multiple dimensions, allowing simultaneous horizontal linking and vertical data depth without the limitations of traditional table-based architectures.
Data Source
AI summary
Disclosed herein is a process that involves retrieving a data model for a data source, establishing at least one filter operation or at least one clean operation that modifies an aspect of the retrieved data model, onboarding underlying data from the data source while applying the established at least one filter operation or at least one clean operation, defining at least one transformation operation to apply to a portion of the underlying data that has been onboarded, and applying the at least one transformation operation to the portion of the underlying data to thereby assign the data to a semantic network, the semantic network comprising conceptual data components and associative data components.