Graph Database Meta-Model for Enterprise Relationship Mapping
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Solution Overview
Problem
Current enterprise technologies lack a formally documented meta-model that streamlines workflows, improves communication, and organizes enterprise information, leading to disorganized data structures that hinder the relationship between technology and business strategy information.
Innovation Solution
A system and method for categorizing data domains based on logical groupings and data velocities, using a graph database with nodes and edges to represent data objects and relationships, and applying machine learning for data analysis and pattern identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is organized using traditional enterprise technology structures, then data storage is maintained, but data organization and relationship tracking between technology and business strategy information deteriorates
Solution Approach 1:
The patent segments enterprise information into distinct data domains (business domain, technology domain, application domain, data domain) with standardized meta-models for each. This segmentation allows systematic organization of technology and business strategy information separately while maintaining their relationships through the graph database structure, resolving the contradiction by preventing information loss through structured categorization without overwhelming complexity.
Solution Approach 2:
The patent introduces a new dimensional framework by implementing a four-layer domain architecture (business, technology, application, data) that adds structural depth to traditional flat data organization. This dimensional change enables tracking relationships between technology and business strategy information across multiple layers, improving information preservation while maintaining manageable complexity through hierarchical organization.
2Productivity
If data domains are categorized according to logical groupings and data velocities, then data organization and retrieval efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by introducing data velocity as a categorization parameter with defined ranges (low, medium, high) representing frequency of data changes. This parameter-based categorization within the domain framework improves data retrieval efficiency by enabling targeted queries based on velocity characteristics, while the standardized parameter definitions prevent system complexity from becoming unmanageable.
Solution Approach 2:
The patent creates a universal domain framework that serves multiple functions simultaneously: organizing data by domain type, categorizing by data velocity, establishing relationships through graph structures, and enabling various query operations. This multi-functional design improves retrieval efficiency across different data types without proportionally increasing complexity, as the same framework handles diverse organizational needs.
3Loss of information
If a graph database structure with nodes and edges is implemented, then relationship representation improves, but implementation complexity increases
Solution Approach 1:
The patent introduces an architectural meta-model as an intermediary layer between the graph database structure and enterprise information. This meta-model provides standardized templates for nodes and edges specific to each domain (business, technology, application, data), enabling accurate relationship representation without requiring complex custom graph structures for each use case. The intermediary meta-model simplifies implementation while preserving relationship information.
Solution Approach 2:
The patent applies parameter changes by defining standardized properties and constraints for nodes and edges within each domain's meta-model. These parameter definitions (such as relationship types, cardinality constraints, and domain-specific attributes) enable the graph database to represent complex enterprise relationships efficiently, improving information preservation while the standardization prevents implementation complexity from becoming unmanageable.
Data Source
AI summary
A system analyzing nodes and data labels 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 generating, a plurality of nodes according to an architectural meta-model for a database; providing, meta-data associated with a data object stored by the generated plurality of nodes from the architectural meta-model for the database; grouping, the generated plurality of nodes into at least one or more sets, the sets including ones of the generated nodes that contain the same data label corresponding to the same set for the data object; and storing, properties of the architectural meta-model that include name-value pairs of data for use on the generated plurality of nodes or one or more relationships.