Query Rewriting Processor for Relational Data Harmonization
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Solution Overview
Problem
Database systems face challenges in efficiently integrating and querying data from multiple disparate sources due to the complexity of database structures and lack of standardized access methods, leading to difficulties in maintaining and executing queries across varied databases.
Innovation Solution
A query rewriting processor that utilizes semantic models, such as RDF knowledge graphs, to capture database structures and interconnections, dynamically translating initial model queries into optimized relational database queries, thereby eliminating the need for hard coding and enhancing data retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is integrated from multiple disparate database sources, then data completeness and information value are improved, but system complexity and query difficulty increase
Solution Approach 1:
The patent introduces a data virtualization layer as an intermediary between users and multiple disparate database sources. This layer provides a unified query interface that automatically handles the complexity of integrating data from different sources, allowing users to access complete information without directly dealing with the underlying system complexity. The virtualization layer translates user queries into appropriate queries for each source database and combines results seamlessly.
2Ease of operation
If standardized access methods are implemented across multiple databases, then ease of operation is improved, but adaptability to different database structures decreases
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting query parameters and transformation rules based on the specific characteristics of each database source. The system maintains a standardized query interface while internally adapting to different database structures through configurable parameters, connection settings, and data transformation rules that can be modified to accommodate various database types and schemas.
3Productivity
If data is replicated in a centralized warehouse, then query efficiency is improved, but storage requirements and maintenance complexity increase
Solution Approach 1:
The patent uses selective copying by creating virtual copies of data access paths rather than physical data copies. The data virtualization layer maintains metadata models that represent the structure and relationships of data across multiple sources, allowing efficient query execution without physically replicating large volumes of data. This approach provides the query efficiency benefits of data warehousing while avoiding the storage and maintenance overhead of full data replication.
Data Source
AI summary
A query rewriting processor (processor) analyzes database semantic models (e.g., RDF knowledge graphs) that capture the interconnections (e.g., foreign and primary key links to other tables) present in a relational database. The processor generates an enriched model query given an initial model query (e.g., a SPARQL query) against the semantic model. The processor generates the enriched model query and translates the enriched model query into a relational database query (e.g., an SQL query). The processor may then pass the relational database query to another system or process (e.g., a data virtualization layer) for execution against the individual relational databases. In this manner, the processor automatically translates queries for information about the relational database structure to a corresponding or matching query for data from the relational database structure.