Unified Data Model for Relational and Schema-less Database Integration
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Solution Overview
Problem
Schema-less databases pose challenges in data modeling and integration with relational database management systems due to their flexible and denormalized structure, making it difficult to translate and manage data effectively.
Innovation Solution
A schema analyzer infers a schema from a schema-less database by identifying statistically significant data fields, creating a unified data model that can be used to integrate with relational databases, allowing for query compatibility and data migration between the two systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a schema-less database is used to provide flexibility in data storage, then adaptability is improved, but device complexity increases due to difficulty in data modeling and integration with relational databases
Solution Approach 1:
The patent introduces a schema analyzer as an intermediary component that infers schema from schema-less database data and generates a unified data model. This unified model serves as a mediator between the schema-less database and relational database management systems, enabling integration without direct complex mapping. The schema analyzer translates between the flexible schema-less format and structured relational formats, resolving the integration complexity while preserving adaptability.
Solution Approach 2:
The system dynamically changes the schema representation parameters by inferring schema structure from actual data samples. Instead of requiring predefined schema parameters, the system analyzes data characteristics (data types, relationships, constraints) and adjusts the schema parameters accordingly, allowing flexible data storage while enabling structured integration through the inferred schema.
2Ease of operation
If schema-less data storage is used to avoid strict structure enforcement, then ease of operation is improved, but measurement precision deteriorates due to difficulty in identifying data field significance
Solution Approach 1:
The schema analyzer implements feedback mechanisms by analyzing data samples and identifying statistically significant data fields based on their occurrence patterns. The system uses this feedback to refine the inferred schema, continuously improving the precision of data field identification. The feedback loop allows the system to distinguish between significant and insignificant fields, enabling precise data modeling from flexible schema-less storage.
Solution Approach 2:
The patent replaces manual schema definition (mechanical approach) with automated statistical analysis. Instead of requiring users to manually identify and define data fields and their significance, the system uses statistical algorithms to automatically analyze data patterns and infer the schema, substituting manual precision-based approaches with automated measurement-based approaches that achieve both ease of operation and measurement precision.
3Adaptability or versatility
If data is stored in schema-less format for flexibility, then adaptability is improved, but loss of information increases due to difficulty in translating data between different database systems
Solution Approach 1:
The system performs preliminary action by inferring the schema and generating the unified data model before actual data translation or migration occurs. This preliminary schema inference captures the essential data structure, relationships, and constraints, preserving information that would otherwise be lost during translation. The unified model serves as a pre-established translation guide that maintains data integrity when moving between schema-less and relational formats.
Solution Approach 2:
The patent creates a copied representation of the schema-less data in the form of an inferred schema and unified data model. This copy preserves the essential structure and meaning of the original flexible data while enabling it to be translated into relational formats. The copying process maintains data fidelity by capturing data types, relationships, and constraints, preventing information loss during cross-system translation.
Data Source
AI summary
Schema-less databases can make data modeling and data management difficult and can detrimentally affect integration with an RDBMS. Inferring a schema from a schema-less database can improve integration by indicating a structure or organization of data in the schema-less database. A schema analyzer can infer a schema by processing data of the schema-less database to identify statistically significant data fields. The schema analyzer then creates a schema that comprises the statistically significant data fields. A data modeler can use the resulting schema along with a schema for a RDBMS to generate a unified data model. A user may submit a query based on the unified data model to obtain results from both databases. The data modeler translates the query from the unified model to be compatible with each of the schemas so that data may be written to or retrieved from each of the schema-less database and the RDBMS.