Graph Database Meta-Models for Standardized Enterprise Data Retrieval
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Solution Overview
Problem
Current enterprise technologies lack a formally documented meta-model that streamlines workflows, improves communication, and provides standardized access to data, resulting in disorganized enterprise information.
Innovation Solution
A system and method for categorizing data into domains based on logical groupings and data velocities, using a graph database with nodes and edges to store data objects and relationships, applying machine learning for data analysis, and utilizing an architectural meta-model to derive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in a standardized structure with meta-model, then data organization and retrieval efficiency is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments enterprise data into distinct domains (financial, operational, strategic) with dedicated meta-models for each. This segmentation allows standardized organization within each domain while avoiding the complexity of a single comprehensive meta-model, thereby improving retrieval efficiency without excessive system complexity.
Solution Approach 2:
The patent implements preliminary action by pre-defining meta-models and data domain structures before data ingestion. This upfront standardization enables efficient data organization and retrieval from the start, rather than requiring complex post-processing to impose structure on unorganized data.
2Adaptability or versatility
If data domains are distributed between multiple applications, then system adaptability and flexibility are improved, but data integration and consistency become more difficult
Solution Approach 1:
The patent creates universal data domain definitions that can be consistently applied across multiple applications. Each data domain (e.g., financial data, operational data) has a standardized meta-model that ensures consistency while allowing the same domain to be distributed and utilized by different applications with varying needs.
Solution Approach 2:
The patent introduces data domains as intermediary layers between applications and the underlying data storage. These domains act as mediators that standardize data representation and relationships, enabling consistent data integration across distributed applications while maintaining flexibility in how applications access and use the data.
3Productivity
If machine learning models are used for data analysis, then analytical capabilities and insights are improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing data into standardized domains with defined relationships before analysis. This pre-organization reduces the computational complexity of subsequent machine learning operations, as the data is already structured and ready for analysis, thereby improving analytical capabilities while reducing processing resources.
Solution Approach 2:
The patent segments data into distinct domains with specific characteristics and relationships. This segmentation allows machine learning models to focus on specific data types and patterns within each domain rather than processing entire enterprise data sets, improving analytical depth while reducing computational resource consumption.
Data Source
AI summary
A system for providing a backlog of architectural information for storage in a database. The system may include at least one processor; and at least one memory configured to execute the instructions to perform operations. The operations may include reporting architectural information corresponding to the graph database; generating the architectural information from one or more data domains, the architectural information being generated based on an architectural dataset within the graph database; and distributing architectural information associated with the data domains, to report insights related to the architectural information via a data visualization software.