Semantic Network Data Filtering for Flexible Secure 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 semantic networks that automatically form associations between data, allowing for flexible data ingestion, adaptation to organizational changes, and efficient data synthesis without redesigning the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data storage systems are used with predefined schemas, 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 introduces a semantic network as an intermediary layer between the data storage system and users. This semantic network automatically forms associations between data elements and provides contextual meaning, allowing users to query data without needing to understand the underlying data architecture. The semantic network translates user queries into appropriate data access operations, resolving the contradiction between flexibility and ease of operation.
Solution Approach 2:
The patent implements dynamic entity relationships that can automatically adapt to organizational changes. The system uses machine learning to dynamically update the semantic network and associations between data elements based on changing business requirements, eliminating the need for manual schema redesign while maintaining data accessibility.
2Adaptability or versatility
If conventional data storage systems with fixed relationships between data tables are used, then data can be stored efficiently, but the systems cannot easily expand horizontally across multiple tables
Solution Approach 1:
The patent replaces fixed relationships between data tables with dynamic entity relationships that can automatically adapt to organizational changes. The system uses machine learning to dynamically update associations between data elements, allowing horizontal expansion across multiple tables without requiring manual redesign of the data structure.
Solution Approach 2:
The system automatically forms associations between data elements and updates the semantic network based on organizational changes, eliminating the need for manual intervention or system redesign. The machine learning components continuously learn from data patterns and automatically adjust relationships, enabling the system to serve itself in adapting to new requirements.
3Productivity
If data warehousing systems with predefined schemas are used, then data can be aggregated and pre-processed, but the systems are rigid and require redesign when organizational changes occur
Solution Approach 1:
The patent implements dynamic schemas that can automatically adapt to organizational changes while maintaining data processing efficiency. The semantic network and machine learning components continuously learn from changing business requirements and automatically update data associations, allowing the system to remain both efficient and adaptable without requiring redesign.
Solution Approach 2:
The system uses feedback from organizational changes to automatically update the semantic network and data associations. Machine learning components monitor changes in business requirements and feed this information back into the system, which then automatically adjusts the data model to maintain both processing efficiency and adaptability.
4Ease of operation
If conventional database systems require users to have prior knowledge of data architecture, then data can be accessed according to the foundational design, but users cannot easily obtain desired outputs without understanding the data structures
Solution Approach 1:
The patent introduces a semantic network as an intermediary that provides contextual understanding of data relationships. This semantic network automatically forms associations between data elements and presents them to users in a meaningful way, allowing users to access and understand data without needing to know the underlying data architecture or structures.
Data Source
AI summary
Disclosed herein is a process that involves receiving an indication of an instance of a semantic network, the semantic network comprising conceptual data components and associative data components, receiving a selection of one or more of the conceptual data components and associative data components of the instance of the semantic network, the selection comprising an indication to block the selected one or more conceptual data components and associative data components or selectively filter the selected one or more conceptual data components and associative data components, and presenting a visualization of the semantic network, the visualization being configured to exclude data related to the selected one or more conceptual data components and associative data components or include data related to the selected one or more conceptual data components and associative data components and exclude data not related to the selected one or more conceptual data components and associative data components.