Relational Database Semantic Query Execution via Table-Valued Functions
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Solution Overview
Problem
Semantic reasoning engines are not easily extensible or scalable, and they consume significant memory and resources while executing semantic queries, often requiring large datasets to be materialized and managed externally.
Innovation Solution
Expressing and executing semantic queries directly within a relational database using table-valued functions and common table expressions, eliminating the need for a dedicated semantic reasoning engine and reducing memory usage by processing queries internally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a dedicated semantic reasoning engine is used to execute semantic queries, then query execution capability is provided, but memory consumption and processing load increase significantly
Solution Approach 1:
The patent merges the semantic reasoning engine functionality directly into the relational database management system. The semantic query processor is integrated with the database engine, allowing semantic queries to be executed using the database's existing query optimization and execution infrastructure. This eliminates the need for a separate dedicated semantic reasoning engine and reduces memory consumption by utilizing the database's internal resources.
Solution Approach 2:
The relational database management system is extended to handle both traditional SQL queries and semantic queries uniformly. The query processor can parse, optimize, and execute multiple types of queries (SQL, semantic rules, recursive queries) through a single integrated engine, making the system multi-functional and eliminating the need for specialized external engines.
2Adaptability or versatility
If a dedicated semantic reasoning engine is deployed, then semantic query processing is enabled, but system complexity and resource requirements increase
Solution Approach 1:
The patent combines the semantic reasoning capabilities with the existing relational database management system. By integrating semantic query processing into the database engine, the system maintains a unified architecture rather than requiring separate components for semantic reasoning and database management, thereby reducing overall system complexity.
Solution Approach 2:
The database management system is designed to handle multiple query types (SQL, semantic rules, recursive queries) through a single universal query processor. This multi-functional approach allows the system to perform semantic query processing without requiring additional specialized engines, reducing system complexity while maintaining versatility.
3Productivity
If semantic queries are executed externally, then query results are obtained, but processing time and resource overhead increase
Solution Approach 1:
The patent merges semantic query execution with the database's native query processing pipeline. By executing semantic queries directly within the database engine rather than externally, the system eliminates the overhead of data transfer and context switching between separate systems, reducing processing time and improving productivity.
Data Source
AI summary
Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.