Read-Optimized Logical Schema Access for Enterprise Data Vectorization
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Solution Overview
Problem
Existing database systems face challenges in managing access control for users, where direct access to create, read, modify, or delete database structures can disrupt operations, and frequent data management operations interfere with other users, necessitating a solution that optimizes read operations while shielding consumers from underlying database structures.
Innovation Solution
A read-optimized data management service provides access to functional pathways or areas using logical schemas that are synchronized with underlying database structures, allowing requests to be evaluated against specific logical resources without direct access to the underlying structures, and includes vectorization and machine learning for predicting missing values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users have direct access to create, read, modify, or delete database structures, then users can perform useful data management operations, but user operations may disrupt other database operations and interfere with other users
Solution Approach 1:
The patent segments database access into two distinct layers: a transactional database layer for data management operations and a read-optimized data warehouse layer for analytical operations. This segmentation allows different types of operations to occur in separate environments, preventing interference between users performing data management and those performing analytical queries.
Solution Approach 2:
The patent introduces a data warehouse as an intermediary layer between the transactional database and analytical consumers. This intermediary captures and stores data in a read-optimized format, allowing analytical operations to be performed without directly accessing or interfering with the underlying transactional database structures.
2Productivity
If frequent data management operations are performed, then data can be updated and maintained, but other database users and applications attempting to manage data are interfered with
Solution Approach 1:
The patent divides the database system into transactional and analytical segments, allowing frequent data management operations to occur in the transactional layer without interfering with analytical operations in the data warehouse layer. Each layer operates independently with its own optimization strategies.
Solution Approach 2:
The patent implements periodic data synchronization from the transactional database to the data warehouse, rather than continuous real-time access. This periodic action allows batch updates to be applied without causing continuous interference to other database operations.
3Loss of information
If consumers directly access underlying database structures, then detailed data can be retrieved, but consumers must understand complex database structures and are exposed to structure changes
Solution Approach 1:
The patent introduces a logical schema layer as an intermediary between consumers and the physical database structures. This logical schema provides a simplified, standardized view of the data that consumers can interact with without needing to understand the complex underlying physical database structures or be affected by their changes.
Solution Approach 2:
The patent creates a copy of the database data in a read-optimized data warehouse with a logical schema that is tailored for analytical consumption. This copy contains the necessary data details while presenting a simplified structure to consumers, isolating them from changes to the original database structures.
4Adaptability or versatility
If a standardized logical schema is used, then consumers are shielded from underlying structure changes, but the logical schema must be synchronized with underlying database structures
Solution Approach 1:
The patent implements periodic synchronization of data from the transactional database to the read-optimized data warehouse, rather than continuous real-time synchronization. This allows the logical schema to remain stable for consumers while updates are applied in batches at scheduled intervals, reducing the time impact of synchronization operations.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for providing access, via a read-optimized database service, to functionally oriented pre-built metadata based logical objects. Each logical object provides access to a set of resources defined by a logical schema relevant to a functional area. Each logical schema is determined based at least in part on a read-optimized, synchronized version of one or more underlying related database structures stored to a read-optimized database accessible via the logical schema using the read-optimized database service, with the logical schema being different than the database schema of the underlying database structures. Requests to the read-optimized database service from a consumer of a particular functional area are evaluated against a particular set of logical resources associated with the functional area and translated to map to relevant underlying database structures, thus eliminating the requirement for consumer to understand underlying complex database structures as well as to shield consumers from underlying database structure changes in the future. Further some of the key text data in reference logical objects can be vectorized for usage in LLM-RAG use cases for assisting in semantic/similarity search of user queries. An attribute defaulting configuration interface and process is also described.