Graph Query Optimization via Satisfiability Prediction
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Solution Overview
Problem
As organizations grow, data silos increase, leading to incompatible data systems that require substantial overhead and disrupt organizational activities during data migration, causing inefficiencies and disruptions.
Innovation Solution
The system employs data conversion models and prediction models to facilitate multi-source-type interoperability by converting data representations between different data sources, optimizing query sets, and predicting query-related resource usage to reduce delays and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data migration systems are used to transfer data from different data storage types and formats into one data system, then data interoperability is improved, but substantial overhead in computational resources and time is required, and significant disruptions to organizational activities occur
Solution Approach 1:
The patent introduces a virtualization layer that acts as an intermediary between diverse data sources and the data analytics system. This virtual layer enables data from different storage types and formats to be accessed uniformly without physical migration, thus maintaining organizational productivity while achieving data interoperability through virtual consolidation rather than physical data movement.
2Reliability
If query sets are executed without optimization, then complete data retrieval is achieved, but query-related resource usage increases
Solution Approach 1:
The patent applies preliminary action by predicting satisfiability issues with query operators before executing the query set. The system analyzes the query plan in advance, identifies operators that will fail to produce satisfactory results, and removes them beforehand. This prevents wasted computational resources on futile query operations while ensuring that only beneficial queries are executed, thus maintaining retrieval completeness for achievable data.
3Productivity
If multiple queries are combined using query operators, then comprehensive data analysis is achieved, but satisfiability issues arise that waste computational resources
Solution Approach 1:
The patent implements feedback by using prediction models that analyze query operator outcomes and provide information about potential satisfiability issues. This feedback mechanism allows the system to identify and remove problematic query operators before execution, preventing computational resource waste while maintaining comprehensive data analysis through the retention of viable query operators.
Data Source
AI summary
In certain embodiments, query-related resource usage in a data retrieval process may be reduced. In some embodiments, a graph query related to a data request may be obtained. The graph query may be transformed into a query set based on a graph data model and patterns of the graph query. Upon generation, the query set may include queries and query operators linking the queries, where the query operators include a first query operator linking first and second queries of the queries or other query operators. Prior to execution of the first and second queries, a satisfiability issue may be predicted, where the satisfiability issue is related to combining results derived from the first and second queries. Based on the prediction, the first query operator may be removed from the query set to update the query set. The updated query set may be executed to satisfy the graph query.