Query Decomposition for Heterogeneous Database Integration
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Solution Overview
Problem
Existing database query systems face challenges when accessing multiple databases with incomplete schema, as they require significant understanding of each database's schema and often result in complex and inefficient queries due to the need for matching all entities referenced by the query.
Innovation Solution
A method is introduced to decompose queries into individual predicates and generate effective queries for each data source by removing predicates involving fields not contained in the source, allowing for the issuance of queries across multiple databases with diverse schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If queries are constructed to access multiple databases with incomplete schema, then data retrieval capability is improved, but query complexity and difficulty of construction increase significantly
Solution Approach 1:
The patent segments a complex query into multiple sub-queries, each targeting a specific database with incomplete schema. The query decomposition process divides the original query into smaller components that can be independently executed against individual databases, then combines the results. This segmentation reduces the complexity of constructing queries against heterogeneous databases while maintaining comprehensive data retrieval capability.
Solution Approach 2:
The patent introduces an intermediary layer (query decomposition and result integration mechanisms) between the user's high-level query and the underlying multiple databases with incomplete schemas. This intermediary automatically handles the complexity of schema matching, query transformation, and result merging, shielding users from the complexity while enabling versatile data retrieval across distributed databases.
2Measurement precision
If unique query statements are written for each database to handle incomplete schema, then data access accuracy is improved, but productivity and efficiency decrease
Solution Approach 1:
The patent creates a universal query processing framework that can handle multiple databases with different schemas through a single high-level query interface. The system automatically adapts the universal query to each specific database's schema, eliminating the need to write unique query statements for each database while maintaining accurate data access. This multi-functional approach improves productivity by allowing users to query heterogeneous databases uniformly.
Solution Approach 2:
The patent performs preliminary actions by pre-decomposing queries and pre-planning the access strategy for multiple databases before actual data retrieval. The system analyzes the query requirements, identifies which databases contain relevant data, and prepares the query execution plan in advance. This preliminary processing enables efficient query execution while ensuring accurate data access across databases with incomplete schemas.
3Adaptability or versatility
If joining logic is added to combine data from multiple databases, then data integration capability is improved, but processing cost and time increase
Solution Approach 1:
The patent segments the data integration process into independent query executions against individual databases, avoiding the need for complex joining operations. By decomposing the query and executing separate queries against each database, the system retrieves data in discrete units that can be efficiently combined without traditional joins, reducing processing time while maintaining integration capability.
Solution Approach 2:
The patent applies partial action by retrieving only the specific data needed from each database based on query decomposition, rather than performing exhaustive joins across entire datasets. The system executes queries selectively against databases that contain relevant data, obtaining partial results that are then integrated. This approach reduces processing time by avoiding unnecessary data retrieval and joining operations while still achieving comprehensive data integration.
Data Source
AI summary
An improved method, system, and article of manufacture for issuing a query against multiple data sources that may not each contain all the fields involved in the query is provided. A diverse query targeting the multiple data sources may be decomposed into individual query predicates. For each data source, an effective query may be generated by removing, from the diverse query, individual predicates involving fields not contained in the data source. Partial results received in response to issuing the effective queries against their corresponding data sources may be merged using any suitable technique.