Ontology Query Routing for Access-Controlled Database Execution
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Solution Overview
Problem
Existing systems struggle to efficiently manage and query ontology-based databases due to the complexity of data processing and access control, leading to suboptimal performance and resource utilization.
Innovation Solution
A system that transforms ontology queries into database queries, estimates data processing requirements, and routes complex queries to high-performance environments like Apache Spark for optimized execution, while managing ontology data across multiple databases with varying capabilities and access controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ontology queries are executed directly on database systems with access control constraints, then security and data protection are maintained, but query performance and processing efficiency deteriorate
Solution Approach 1:
The patent introduces an intermediary query execution system that sits between the user and the database. This intermediary transforms high-level ontology queries into optimized database queries, estimates data processing requirements, and routes queries to appropriate execution environments (local or distributed Spark clusters). This mediator layer maintains security through access control while improving performance through intelligent query optimization and parallel processing.
Solution Approach 2:
The patent segments the query execution process into distinct phases: query transformation, data processing estimation, routing decision, and execution. It also segments the execution environment into local database execution for simple queries and distributed Spark cluster execution for complex queries requiring large-scale data processing. This segmentation allows each component to be optimized independently, maintaining security while improving overall performance.
2Measurement precision
If complex data processing is performed to handle ontology queries with access controls, then query accuracy and completeness are improved, but resource utilization and processing time worsen
Solution Approach 1:
The patent performs preliminary actions by estimating the amount of data processing required before actual query execution. The system analyzes the ontology query, determines the scope of data access needed, and predicts resource requirements in advance. This preliminary estimation allows the system to make informed routing decisions and prepare appropriate execution environments, ensuring accurate results while minimizing processing time through proactive resource allocation.
Solution Approach 2:
The patent implements dynamic query routing that adapts to the specific requirements of each query. Based on the estimated data processing needs and current system state, the system dynamically decides whether to execute queries locally or route them to distributed Spark clusters. This dynamic approach ensures that queries requiring high accuracy get sufficient processing resources while minimizing overall processing time through intelligent load balancing.
3Adaptability or versatility
If ontology data is managed across multiple databases with different capabilities, then data accessibility and flexibility are improved, but system complexity and management difficulty worsen
Solution Approach 1:
The patent creates a universal query interface that works across multiple diverse database systems. The system translates high-level ontology queries into database-specific queries, allowing users to access data from multiple databases with different capabilities through a single unified interface. This multi-functionality approach maintains data access flexibility while hiding the underlying complexity of managing multiple database systems.
Solution Approach 2:
The patent introduces an intermediary layer that manages the complexity of multiple databases. This layer handles query transformation, data routing, and coordination between different database systems. By placing this mediator between the user and multiple databases, the system provides flexible data access while centralizing the management complexity in a single component that can be optimized and maintained independently.
Data Source
AI summary
A system for efficient query execution over ontology-based databases and related methods are disclosed. The system is programmed to receive an ontology query from a user account against an ontology having access controls, and transform the ontology query into a set of database queries, including a set of exact aggregations or joins. The system is programmed to then estimate how much data processing is to be performed in executing the set of exact aggregations or joins, considering how much data in the ontology can be accessed by the user account under the access controls. Upon determining that the estimated amount of data processing exceeds a threshold, the system is programmed to route the set of exact aggregations or joins to a set of nodes optimized for large-scale data processing.