Synthetic Graph Tables for Relational Data Analysis
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Solution Overview
Problem
Current methods for applying graph analysis to data sets stored in relational databases are inefficient and error-prone, as they require manual conversion to graph representations, which can be time-consuming and prone to errors, and existing graph databases struggle with analytic workloads and transactional performance.
Innovation Solution
A system that maps tables and columns of a legacy relational schema into synthetic node and edge tables, allowing for automatic transformation and querying of data as a graph, using machine-readable descriptors like XML or JSON, and enabling efficient graph query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual conversion to graph representation is used, then graph analysis can be performed, but the process is time-consuming and error-prone
Solution Approach 1:
The system pre-processes relational data into graph representation format in advance, creating synthetic node and edge tables that can be directly used for graph analysis. This preliminary transformation eliminates the need for manual conversion during analysis, significantly reducing time loss while maintaining accuracy.
Solution Approach 2:
The system automatically performs the conversion from relational to graph representation without requiring manual intervention. The automated process uses defined schemas and algorithms to transform data, eliminating human error and time consumption associated with manual conversion while enabling rapid graph analysis.
2Adaptability or versatility
If graph databases are used to manage data as a graph model, then graph analysis is enabled, but transactional performance and analytic workload handling deteriorate
Solution Approach 1:
The system segments the data storage and processing into two distinct parts: relational tables for maintaining transactional data with high performance, and synthetic graph representation for enabling graph analysis. This segmentation allows each system to operate in its optimal performance zone without compromising the other.
Solution Approach 2:
The system introduces synthetic node and edge tables as intermediary structures that bridge relational data and graph analysis requirements. These intermediary tables enable graph analysis capabilities while the underlying relational database continues to handle transactions efficiently, thus resolving the performance trade-off.
3Productivity
If specialized graph analysis frameworks are used, then graph analysis performance is improved, but the complexity of configuring graph representation increases
Solution Approach 1:
The system automatically generates graph representations from relational schemas without requiring manual configuration by users. The automated process interprets relational structures and creates appropriate graph models, eliminating the complexity of manual setup while maintaining high graph analysis performance through optimized frameworks.
Data Source
AI summary
Techniques are provided for mapping tables and columns of a legacy relational schema into synthetic tables that are dedicated for graph analysis. In an embodiment, a computer receives a mapping of relational tables to node tables and edge tables. The node tables contain columns and rows. The edge tables contain columns and rows. The rows of the node tables and the rows of the edge tables define a graph. Based on the mapping and the relational tables, the computer calculates a value of at least one column of at least one row of the node tables. Based on an execution of a query of the graph, the computer returns the value.