Neural Network Graph Data Model Generation
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Solution Overview
Problem
As organizations grow, data silos increase, leading to incompatible data systems that require substantial overhead and disrupt organizational activities during migration, hindering multi-source-type interoperability and information retrieval optimization.
Innovation Solution
The use of machine-learning-model-based generation of graph data models to convert non-graph data representations into compatible formats, prediction models for data conversion and storage, and query set optimization to reduce resource usage and latency in data retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data migration systems are used to transfer data from different data storage types and formats into one data system, then data interoperability is improved, but substantial overhead in computational resources and time is required, and significant disruptions to organizational activities occur
Solution Approach 1:
The patent introduces a virtualization layer that acts as an intermediary between diverse data sources and the data system. This virtualization layer enables data from different storage types and formats to be accessed and integrated without physical migration, thus improving data interoperability while avoiding disruptions to organizational activities. The intermediary layer translates and harmonizes data access requests without requiring data movement.
Solution Approach 2:
The patent implements predictive analytics and pre-computation of data relationships and access patterns. By anticipating future data retrieval needs and pre-processing data in advance, the system reduces computational overhead during actual data access operations. This preliminary action allows the system to handle diverse data sources efficiently without requiring substantial computational resources during peak operations.
2Adaptability or versatility
If data migration processes are implemented to unify incompatible data systems, then data compatibility is improved, but significant time and computational resources are consumed
Solution Approach 1:
The patent creates virtual copies and representations of data from various sources within a unified data model structure. Instead of physically migrating and transforming actual data, the system generates virtual data models that replicate the structure and relationships of source data. This copying approach achieves data compatibility instantly without time-consuming migration processes, as the virtual models can be created and configured rapidly.
Solution Approach 2:
The virtualization layer serves as an intermediary that provides compatibility between incompatible data systems through standardized access interfaces. This layer translates diverse data formats and structures into a unified model without requiring actual data conversion or migration, thereby achieving data compatibility while minimizing time investment.
3Productivity
If comprehensive data migration is performed to eliminate data silos, then information retrieval efficiency is improved, but substantial computational overhead and organizational disruption occur
Solution Approach 1:
The patent segments the data integration approach into a virtualization layer that operates independently from source data systems. This segmentation allows the system to provide unified data access without migrating all data centrally. The virtualization layer processes and manages data relationships in a distributed manner, reducing computational overhead while maintaining information retrieval efficiency across segmented data sources.
Solution Approach 2:
The system performs preliminary analysis and pre-computation of data relationships, access patterns, and integration mappings. By anticipating data retrieval needs and pre-processing integration logic in advance, the system reduces computational overhead during actual operations. This preliminary action enables efficient information retrieval across previously siloed data sources without requiring continuous substantial computational resources.
Data Source
AI summary
In some embodiments, templates related to each graph data model of a graph data model set for converting non-graph data representations in a non-graph database to graph data representations compatible with a graph database may be obtained. One or more templates and the non-graph data representations may be provided to a neural network for the neural network to predict additional templates. The additional templates may be provided to the neural network as reference feedback for the neural network's prediction of the additional templates to train the neural network. A collection of non-graph data representations from a given non-graph database may be provided to the neural network for the neural network to generate one or more templates for a given graph data model for converting non-graph data representations in the given non-graph database into graph data representations compatible with a given graph database.