Query Optimization for Data Interoperability
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Solution Overview
Problem
Large enterprises face challenges in interoperability between incompatible data systems, leading to data silos and significant overhead in data migration, causing disruptions and resource inefficiencies.
Innovation Solution
A system that uses data conversion models and prediction models to facilitate multi-source-type interoperability, optimizing query sets and reducing resource usage by transforming queries and storing data in temporary storage for efficient retrieval.
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 interoperability between incompatible data systems is improved, but substantial overhead in computational resources and time is required, causing significant disruptions to organizational activities
Solution Approach 1:
The patent introduces a data virtualization layer that acts as an intermediary between incompatible data systems. This layer enables queries to be routed to multiple heterogeneous data sources simultaneously without requiring physical data migration, thus maintaining interoperability while avoiding disruptions to organizational activities.
Solution Approach 2:
The patent segments the data access architecture into separate logical components: a query interface layer, a virtualization layer that manages multiple data sources, and the underlying heterogeneous data systems. This segmentation allows each component to operate independently, enabling interoperability without forcing all systems to undergo migration.
2Adaptability or versatility
If data migration systems are used to transfer data from different data storage types and formats, then interoperability between incompatible data systems is improved, but substantial computational resources and time overhead are required
Solution Approach 1:
The patent performs preliminary actions by pre-compiling and caching query execution plans in the data virtualization layer. When queries are executed, the system retrieves pre-prepared plans rather than performing full data retrieval and transformation computations each time, significantly reducing computational resource overhead while maintaining interoperability.
Solution Approach 2:
The patent creates virtual copies of data access pathways through the data virtualization layer. Instead of physically moving or transforming data between systems, the system creates and manages virtual representations of data access routes, allowing queries to be resolved through metadata and execution plans without substantial computational transformation overhead.
3Reliability
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 partial action by optimizing query sets to execute only the necessary subset of queries required to satisfy the original data request. The system analyzes query dependencies and eliminates redundant queries, achieving complete necessary data retrieval while reducing overall resource usage by performing fewer query operations.
Data Source
AI summary
In certain embodiments, 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.