Dynamic Schema Tables for Rapid Database Evolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The relational database model requires explicit schema definition, which hinders rapid application development and schema evolution, as it necessitates downtime and database migration for schema changes, and existing flexible fields approaches are clumsy and error-prone.
Innovation Solution
Implementing dynamic schema tables that allow for the creation of new columns dynamically in response to Data Manipulation Language (DML) SQL statements, storing these columns as key-value pairs in an invisible-container column, enabling flexible and rapid schema evolution without explicit schema adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit schema definition is used in relational databases, then data usability and query capabilities are improved, but schema evolution requires downtime and database migration
Solution Approach 1:
The patent implements dynamic schema evolution by allowing column definitions to be modified without requiring database downtime. The schema is no longer static but can evolve dynamically through DML statements, enabling continuous operation while adapting to changing application requirements.
Solution Approach 2:
The system performs preliminary schema validation and preparation before applying changes. The DBMS prepares the schema evolution in advance, validates the proposed changes, and then applies them atomically to avoid downtime, ensuring data usability is maintained throughout the process.
2Productivity
If schema-less data model is used, then rapid application development is enabled, but explicit schema must be developed later which is complex and time-consuming
Solution Approach 1:
The system enables self-service schema evolution where the database automatically adapts its schema based on DML statements without requiring external schema definition processes. Applications can directly modify the schema through data operations, eliminating the need for separate schema development phases.
Solution Approach 2:
The patent introduces an intermediary layer between the application and the physical schema storage. This intermediary processes DML statements and automatically translates them into schema modifications, bridging the gap between flexible application development and structured data storage without requiring explicit schema definitions.
3Productivity
If flexible fields approach with pre-created generic columns is used, then performance advantage of relational columns is realized, but the approach is clumsy and error-prone
Solution Approach 1:
Instead of requiring manual selection and configuration of generic columns, the system enables self-service where the schema automatically adapts to application needs through DML statements. The database autonomously creates and manages columns based on actual data operations, eliminating manual intervention errors.
Solution Approach 2:
The patent transforms the static flexible fields approach into a dynamic system where columns are created and modified on-demand based on actual application requirements. Rather than pre-defining generic columns, the schema evolves dynamically to match the application's data needs, improving both ease of operation and accuracy.
Data Source
AI summary
In an artificial neural network, integrality refers to the degree to which a neuron generates, for a given set of inputs, outputs that are near the border of the output range of a neuron. From each neural network of a pool of trained neural networks, a group of neurons with a higher integrality is selected to form a neural network tunnel (“tunnel”). The tunnel must include all input neurons and output neurons from the neural network, and some of the hidden neurons. Tunnels generated from each neural network in a pool are merged to form another neural network. The new network may then be trained.