Homomorphic Key Materialization for Semi-Structured DB Indexing
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Solution Overview
Problem
Relational databases face challenges in storing semi-structured data due to the inherent flexibility and variability of JSON and other data formats, which lack a fixed schema, leading to inconsistent and missing identifiers, making it difficult to manage and index such data efficiently.
Innovation Solution
Generate index-friendly homomorphic key values for semi-structured data documents by determining and extracting an identifier field, converting it to a raw format, and storing it as a primary key in the relational database management system, delegating the generation of missing identifiers to the DBMS to reduce computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semi-structured data is stored in relational databases, then data flexibility and adaptability are improved, but data consistency and identifier reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by extracting and materializing primary key values from semi-structured data before storage. The DBMS automatically determines identifier fields, validates their presence, and generates materialized primary keys in advance, ensuring reliability is established prior to actual data storage operations.
Solution Approach 2:
The patent introduces an intermediary layer between semi-structured data and relational database requirements. The DBMS acts as this intermediary by automatically extracting identifiers, validating them against schema constraints, and generating materialized primary keys, thereby mediating between the flexibility of semi-structured data and the consistency requirements of relational databases.
2Manufacturing precision
If schema validation is performed before storage, then data quality and consistency are improved, but processing time and system complexity increase
Solution Approach 1:
Schema validation and primary key materialization are performed as preliminary actions during the data extraction phase. The DBMS validates identifier fields and materializes primary keys before actual storage operations, ensuring data quality is established in advance rather than during processing.
Solution Approach 2:
The system implements self-service by allowing the DBMS to automatically perform schema validation and primary key generation without requiring explicit application-level validation logic. The database system serves itself by extracting, validating, and materializing primary keys autonomously during the insert operation.
3Reliability
If application logic is used to extract and validate identifiers, then data consistency is improved, but device complexity and programming overhead increase
Solution Approach 1:
The DBMS performs self-service by automatically extracting identifier fields, validating them against schema constraints, and materializing primary keys without requiring application-level intervention. This eliminates the need for complex application logic while maintaining data consistency through database-level automation.
Solution Approach 2:
The patent replaces mechanical application-level processing with database-level automation. Instead of using application programming to extract and validate identifiers, the system substitutes this with DBMS-native operations that automatically handle schema validation and primary key materialization, reducing programming overhead while maintaining reliability.
4Reliability
If multiple round-trips to DBMS are performed for schema verification, then schema compatibility is improved, but network resources and processing overhead increase
Solution Approach 1:
Schema validation is performed as a preliminary action during the initial data extraction phase rather than through multiple subsequent round-trips. The DBMS validates schema compatibility in advance, ensuring that once data is extracted and materialized, no additional verification round-trips are needed, thereby reducing network resource consumption.
Data Source
AI summary
Techniques are described for generating index-friendly homomorphic key values for indexing semi-structured data documents in a collection when storing the collection in a relational database management system (DBMS). In an implementation, a request is received from a client application to store a semi-structured data document collection into a target table of the relational DBMS. Without ascertaining any schema information for a document in the collection, the techniques determine whether the document fails to include an identifier that uniquely identifies the document in the collection. Techniques further include generating a unique identifier that identifies the document in the collection, if no identifier exists, and materializing the identifiers of the collection into indexable primary key values for the target relational table.