Adjacency Structures for Graph Algorithms in Relational Databases
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Solution Overview
Problem
Relational databases face inefficiencies in processing graph-modeled data due to the need for external processing engines and the redundancy of secondary indexes, which can hinder the execution of graph algorithms.
Innovation Solution
Implementing an adjacency structure within the relational database, such as an adjacency list or matrix, to enable efficient execution of graph algorithms by storing vertices and edges in separate tables and dynamically generating or updating this structure based on performance metrics and changes in the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If graph algorithms are executed in a relational database using traditional table structures, then data storage is maintained with standard relational schemas, but execution efficiency deteriorates due to the need for external processing engines and redundant secondary indexes
Solution Approach 1:
The patent merges the graph data storage and adjacency structure into the relational database system itself, eliminating the need for separate external processing engines. The adjacency structure is integrated as a specialized data structure within the database, combining relational storage with graph algorithm execution capabilities in a unified system.
Solution Approach 2:
The patent extracts the adjacency structure from traditional relational table structures, creating a specialized data structure that specifically represents graph relationships. This adjacency structure is separated from the standard vertex/edge tables to provide efficient graph algorithm execution without relying on redundant secondary indexes.
2Productivity
If an adjacency structure is generated and maintained within the relational database, then graph algorithm execution efficiency is improved, but computational resources and storage requirements increase
Solution Approach 1:
The adjacency structure is dynamically generated and maintained within the relational database based on the specific graph data and algorithm requirements. The structure adapts to changes in the graph-modeled data, being regenerated or updated when data changes are detected, thereby optimizing performance for current data states without wasting resources on stale or irrelevant adjacency information.
Solution Approach 2:
The system changes the storage and computation parameters by introducing the adjacency structure as a specialized data representation. This parameter change enables efficient graph algorithm execution by transforming the data structure from traditional relational tables to an adjacency-based format that is optimized for graph processing operations.
3Reliability
If the adjacency structure is updated in response to data changes, then data currency is maintained, but processing time and computational overhead increase
Solution Approach 1:
The system implements a feedback mechanism where changes in graph-modeled data are detected and trigger updates to the adjacency structure. This feedback loop ensures that the adjacency structure remains current with the underlying data, maintaining data currency and reliability for subsequent graph algorithm executions.
Solution Approach 2:
The adjacency structure is pre-generated and pre-maintained within the database schema, allowing for rapid access during graph algorithm execution. By preparing the adjacency structure in advance and maintaining it through automated updates, the system reduces the time required during actual algorithm execution while ensuring data currency.
Data Source
AI summary
A system for processing graph-modeled data in a relational database is provided. The system can include at least one data processor and at least one memory storing instructions that are executed by the at least one data processor. Executing the instructions can result in operations comprising: receiving a request to execute a graph algorithm operating on graph-modeled data stored at a relational database; and executing the graph algorithm within the relational database, the executing comprising use of an adjacency structure within the relational database. Related methods and articles of manufacture, including computer program products, are also provided.