Automated Graph Database Object Synchronization from Relational Data
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Solution Overview
Problem
Integrating existing enterprise applications into an Enterprise Knowledge Graph (EKG) is challenging and resource-intensive, particularly due to the complexity of capturing varied and extensive relationships between datasets, and requires manual updates after changes in relational databases.
Innovation Solution
An automated process for creating and synchronizing graph database objects from relational databases, allowing for the creation of Graph Data Objects and Graph Data Relations that represent enterprise data objects and their relationships, enabling seamless integration and bi-directional linking between data objects and graph objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual integration methods are used to capture relationships between datasets, then integration accuracy can be maintained, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system automatically detects and captures relationships between datasets by querying multiple data sources and analyzing their interconnections without requiring manual configuration. The framework self-adapts to the data landscape, automatically creating graph objects and relationships when data sources are added or modified
Solution Approach 2:
The system dynamically adjusts its relationship capture parameters based on the specific data sources being integrated. It queries data sources with appropriate parameters to discover relationships, and automatically modifies the graph structure to reflect the detected relationships between entities across different data sources
2Reliability
If the graph database is automatically synchronized with relational databases, then data consistency is improved, but the synchronization overhead increases
Solution Approach 1:
The system implements bidirectional linking between graph objects and data objects, creating a feedback mechanism where changes in either system automatically trigger synchronization operations. When a data object changes in the relational database, the corresponding graph object is updated, and vice versa, maintaining real-time consistency
Solution Approach 2:
The system pre-establishes the bidirectional linking structure and synchronization protocols in advance, so that when changes occur, the synchronization can proceed efficiently through pre-configured pathways rather than requiring complex real-time analysis
3Manufacturing precision
If developers can control data replication on a semantic level, then data accuracy is improved, but the configuration complexity increases
Solution Approach 1:
The system allows developers to control data replication at the individual graph object level rather than requiring global configuration. Each graph object can have independent replication settings, enabling precise control over which specific data elements are replicated to the graph database while maintaining simplicity for standard cases
Data Source
AI summary
A request is received to create a graph database from one or more relational databases. For each relational database, data objects in the relational database are identified. For each data object, a graph data object corresponding to the data object is created. The graph data object is linked to the data object. A set of associated data objects in the relational database are determined, and for each associated data object, an associated graph data object is created if a graph data object corresponding to the data object does not exist. For each created graph data object, a graph data relation object is created that represents a relationship between the graph data object and the associated graph data object. Created graph data objects, associated graph data objects, and graph data relation objects are stored in the graph database. The graph database is provided to one or more applications.