Database Management System Schema Mapping Rule Graph Query
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Solution Overview
Problem
Current approaches for querying graphs on relational data require data duplication, are not suitable for real-time processing, and rely on simple schema mapping, which limits their effectiveness and efficiency.
Innovation Solution
A database management system and method that uses a rule-based domain-specific language for conceptual schema mapping to transform relational data into graph data, allowing for on-the-fly inference of relationships and eliminating the need for redundant graph storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is duplicated into graph storage, then graph query performance is improved, but storage requirements and system complexity increase
Solution Approach 1:
The patent merges the relational data storage with graph data storage by using the same physical storage infrastructure. The graph data is stored as additional columns or attributes within the existing relational tables, eliminating the need for separate graph storage systems while maintaining fast graph query performance through optimized query execution plans.
Solution Approach 2:
The patent creates a multi-functional storage system that serves both relational data access and graph data access through a single storage infrastructure. The same data storage is used for traditional SQL queries and graph traversal queries, reducing overall storage requirements while providing universal access to both data models.
2Speed
If data is duplicated into graph storage, then graph query performance is improved, but device complexity increases
Solution Approach 1:
The patent combines graph storage capabilities with existing relational database storage systems, eliminating the need for separate graph database systems. This merging approach maintains fast graph query performance while reducing system complexity by consolidating storage infrastructure and management operations.
3Reliability
If ETL processes are used to convert relational data to graph entities, then graph data consistency is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary transformation of relational data into graph entities during data insertion or update operations, rather than through separate batch ETL processes. This preliminary action ensures data consistency is maintained from the point of data creation, eliminating the need for time-consuming periodic ETL synchronization processes.
Solution Approach 2:
The database system automatically performs the transformation from relational data to graph entities without requiring external ETL tools or processes. The system self-services by intercepting data operations and automatically maintaining both relational and graph representations of the data, ensuring consistency without additional processing time.
4Ease of manufacture
If simple schema mapping is used, then implementation ease is improved, but mapping effectiveness deteriorates
Solution Approach 1:
The patent implements a dynamic schema mapping system that automatically adapts the mapping between relational schemas and graph schemas based on the specific data and query requirements. Rather than using fixed simple mapping rules, the system dynamically determines the optimal mapping strategy, improving mapping effectiveness while maintaining ease of implementation through automation.
Solution Approach 2:
The patent changes the parameters of schema mapping from static, predefined rules to dynamic, context-aware transformations. The mapping effectiveness is improved by adjusting mapping parameters based on data characteristics, relationship types, and query patterns, while the implementation remains easy through automated parameter optimization.
Data Source
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AI summary
The disclosure relates to a database management system (400) for managing a database (405). The database management system comprises a processor (411) configured to generate a schema mapping rule (411b), based on a definition (410) of at least one of a graph vertex and a graph edge of data (405b) of the database. The schema mapping rule defines a correspondence between the data of the database and a graph representation of the data of the database, the graph representation of the data including the at least one of the graph vertex and the graph edge of the data of the database. The processor is further configured to generate, based on the schema mapping rule, a database query (411e) for extracting one or more further graph vertices and/or graph edges of the data of the database. In particular, the disclosure may be applied to a relational database management system.