Graph Database Update System Using Selective Inference Rules
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Solution Overview
Problem
Graph databases require frequent updates for online or near-online analysis, which is computationally expensive due to the need to rerun inference rules across all foundational relationships, leading to excessive resource usage and inefficiency.
Innovation Solution
A method is introduced that identifies a set of foundational relationships for deletion or storage, infers additional relationships using specific inference rules, and updates the graph database by iteratively applying these rules only to the relevant relationships, reducing the computational resources required by focusing on changes rather than the entire database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference rules are rerun across all foundational relationships to update the graph database, then data consistency is maintained, but computational resources and processing time increase excessively
Solution Approach 1:
The patent segments the inference process by identifying and processing only the subset of relationships affected by updates, rather than rerunning inference rules across all foundational relationships. This is achieved by tracking which relationships are modified and limiting inference operations to those specific segments, thereby reducing computational overhead while maintaining data consistency.
Solution Approach 2:
The patent applies partial action by performing inference operations only on the necessary subset of relationships that are actually affected by updates, rather than executing complete inference cycles on the entire database. This selective approach reduces resource consumption while ensuring that all necessary inferences are made for the updated portions.
2Measurement precision
If inference rules are applied to all foundational relationships during updates, then complete data accuracy is achieved, but processing time becomes excessive
Solution Approach 1:
The patent divides the inference processing into segments based on which relationships are actually modified. By identifying the specific subset of relationships affected by updates and limiting inference operations to those segments, the system achieves complete accuracy for updated data while avoiding unnecessary processing time on unchanged relationships.
3Productivity
If the graph database is updated frequently for online analysis, then data freshness is improved, but resource usage increases excessively
Solution Approach 1:
The patent enables frequent updates by applying inference rules only to the partial set of relationships that are actually modified, rather than performing complete inference cycles on the entire database. This selective approach reduces resource consumption per update, making frequent updates feasible for online analysis while maintaining data freshness.
Data Source
AI summary
A computer-implemented method includes receiving one or more update requests; identifying, a set of foundational relationships for deletion from a graph database; inferring, using one or more relationship inference rules, a set of inferred relationships for deletion from the graph database; and deleting, from the graph database, the set of foundational relationships for deletion and the set of inferred relationships for deletion. Inferring the set of inferred relationships for deletion includes searching for relationships in the set of foundational relationships for deletion that match a relationship specified in a precondition of at least one of the one or more relationship inference rules; determining whether the precondition is satisfiable using at least one of the matching relationships; and, in response to the precondition being satisfiable, inferring at least one of the relationships of the set of inferred relationships for deletion using at least one of the matching relationships.