Virtual Adjacency Matrix for Graph Database Logic Rules
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Solution Overview
Problem
Existing methods for processing logic rules in graph databases are complex, costly in performance, and require dedicated query engines, making them inefficient for interactive applications.
Innovation Solution
A computer-implemented method that processes logic rules in a graph database by generating a virtual adjacency matrix combining existing adjacency matrices and entailed data matrices, allowing for efficient query processing without additional complexity in the query engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for processing logic rules in graph databases are used, then complete logic rule processing is achieved, but performance cost is high and query engine complexity increases
Solution Approach 1:
The patent segments the adjacency matrix into two distinct parts: the original adjacency matrix (R-Matrix) containing explicit graph data, and the entailed data matrix (E-Matrix) containing inferred data from logic rule application. This segmentation allows the system to process only relevant portions of data separately, improving query performance while maintaining complete logic rule processing capability.
Solution Approach 2:
The patent introduces a virtual adjacency matrix as an intermediary structure that combines the R-Matrix and E-Matrix. This virtual matrix serves as a mediator between the original graph data and the logic rule inference engine, enabling efficient query processing without requiring the query engine to handle the full complexity of logic rule processing directly.
2Reliability
If existing methods for processing logic rules in graph databases are used, then complete logic rule processing is achieved, but device complexity increases
Solution Approach 1:
The patent extracts the logic rule processing functionality from the query engine by computing the entailed data matrix separately. The E-Matrix contains all inferred data that would otherwise require complex logic rule processing during queries. By taking out this processing step, the query engine complexity is reduced while maintaining complete logic rule processing capability.
Solution Approach 2:
The patent performs logic rule processing in advance by computing the entailed data matrix before query execution. This preliminary action pre-computes all inferred relationships, so that during query processing, the system only needs to retrieve and combine pre-computed results from the E-Matrix with the original R-Matrix data, significantly simplifying the query engine's workload.
3Productivity
If virtual adjacency matrix with entailed data matrix is generated, then query processing efficiency is improved, but data storage requirements increase
Solution Approach 1:
The patent merges the R-Matrix and E-Matrix into a single virtual adjacency matrix structure that can be processed together during queries. This merging allows efficient query processing by treating inferred and explicit data uniformly, while the underlying separate storage of R-Matrix and E-Matrix maintains storage efficiency by avoiding redundant data.
Data Source
AI summary
A computer-implemented method for processing a logic rule in a graph database. The method includes obtaining a graph database comprising at least one graph, each graph of the database being represented in one or more adjacency matrices (R-Matrix), each adjacency matrix representing a group of tuples of the graph comprising a same predicate, obtaining the logic rule concluding to a head predicate, generating a virtual adjacency matrix comprising one of the one or more adjacency matrices (R-Matrix) and an entailed data matrix (E-Matrix), the virtual adjacency matrix representing the head predicate, the entailed data matrix representing a group of tuples that are computed by applying the logic rule, and receiving a query by the database using the head predicate.