Semantic Query Processing for Relational Database Integration
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Solution Overview
Problem
Relational database management systems face challenges in data integration due to data mismatch issues arising from different terms having the same or similar semantic meanings, which can affect query processing and data retrieval accuracy.
Innovation Solution
A computer-implemented method and system that relate attributes of a relational database to their semantic equivalents by using semantically aware operations such as semantic select, project, join, union, and intersection, facilitated through category and master tables that store semantic equivalents, allowing for enhanced query processing in SQL-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SQL-based query processing is used, then system simplicity and ease of operation are maintained, but data retrieval accuracy deteriorates due to data mismatch from different terms having the same semantic meaning
Solution Approach 1:
The patent introduces semantic equivalence tables and a semantic processing layer as intermediaries between the traditional SQL query system and the data. These tables store semantic relationships between different terms, allowing the system to resolve data mismatch issues without fundamentally changing the underlying relational database structure. The semantic processor acts as a mediator that translates semantic queries into traditional SQL queries while handling synonym resolution.
Solution Approach 2:
The patent segments the query processing system into distinct components: a traditional SQL processing engine and a new semantic processing layer. The semantic layer handles specific semantic operations (semantic select, project, join, union, intersection) while the traditional engine handles standard SQL operations. This segmentation allows the system to maintain simplicity for standard queries while adding semantic capabilities where needed.
2Adaptability or versatility
If semantically aware operations are added to handle synonyms, then data integration capability improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent creates a universal query processing framework that can handle both traditional SQL queries and semantic queries through a unified architecture. The semantic processor is designed to work with standard relational database operations, allowing the same system to serve multiple purposes: traditional data retrieval and semantic-aware data integration, without requiring separate systems for each function.
Solution Approach 2:
The patent pre-computes and stores semantic equivalence relationships in semantic equivalence tables before query execution. By preparing semantic mappings in advance rather than computing them during query processing, the system reduces the complexity of real-time semantic processing while maintaining high data integration capability.
3Measurement precision
If semantic equivalence tables are created to map different terms, then query processing accuracy improves, but loss of time occurs during query execution due to additional processing steps
Solution Approach 1:
The patent pre-computes semantic equivalence relationships and stores them in lookup tables during database setup or maintenance periods. During actual query execution, the system performs simple table lookups rather than complex semantic analysis, significantly reducing query execution time while maintaining high accuracy in resolving synonyms and related terms.
Solution Approach 2:
The patent applies semantic processing selectively only when needed - specifically when queries involve terms that may have semantic equivalents. The system can determine whether semantic processing is necessary based on the query structure and data types, avoiding the overhead of semantic processing for queries that don't require it, thus balancing accuracy with execution speed.
Data Source
AI summary
A semantically aware relational database management system includes suitable programming to relate attributes of the relational database to semantic equivalents of such attributes. In response to receiving a query, the relational database management system performs at least one semantically aware operation on the data in the relational database in order to determine what data is to be retrieved in response to the query. Results of the query presented to a user may include data derived from performing the semantically aware operations.