Database Structure Graph for Automated Table Augmentation
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Solution Overview
Problem
Relational databases face challenges in efficiently managing the growth of complex database environments due to large numbers of tables and intricate relationships, making it difficult to accurately determine how to augment existing tables for new entities or relationships.
Innovation Solution
The implementation of a method that uses a database structure graph to classify nodes and edges, determining target sets of key and non-key fields to selectively augment database tables based on these classifications, ensuring accurate and automated table augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods are used to manage relational database growth, then productivity and accuracy of table augmentation improve, but device complexity increases due to the need for graph-based classification systems
Solution Approach 1:
The patent introduces a database structure graph as an intermediary representation layer between the physical database tables and the augmentation logic. This graph models fields as nodes and relationships as edges, serving as a mediator that simplifies the complex task of determining which tables to augment. The graph structure provides a unified view of the database schema that enables automated decision-making without requiring complex analysis of individual table relationships.
Solution Approach 2:
The system changes the parameter representation of database schema from traditional table-centric views to graph-based field-centric views. By representing fields as nodes and relationships as edges, the system transforms the problem space, enabling new algorithms for determining table augmentation targets. This parameter transformation allows the system to efficiently classify nodes and determine key field multisets that guide the augmentation process.
2Manufacturing precision
If manual methods are used to determine table augmentation, then device complexity remains low, but manufacturing precision and reliability of database integrity deteriorate
Solution Approach 1:
The system implements self-service through automated classification of graph nodes and automatic determination of target table sets. The algorithm independently analyzes the database structure graph, classifies nodes based on their roles (root, child, logical combination), and identifies which tables require augmentation without human intervention. This automated self-service ensures consistent and accurate table selection while eliminating manual errors.
Solution Approach 2:
The system uses feedback loops where the classification of graph nodes informs the selection of target tables, which in turn determines the augmentation actions. The multi-set of key fields derived from node classification provides feedback that guides the precise selection of tables to augment, ensuring that only relevant tables are modified while maintaining database integrity constraints.
3Reliability
If comprehensive table augmentation is performed to ensure completeness, then reliability of database consistency improves, but loss of time increases due to evaluating all database tables
Solution Approach 1:
The patent segments the database evaluation process by classifying graph nodes into different types (root nodes, child nodes, logical combination nodes) and determining distinct target sets for each classification. This segmentation allows the system to focus augmentation efforts only on relevant table subsets rather than evaluating all tables uniformly. The segmented approach maintains consistency reliability by ensuring all necessary tables are covered while reducing time loss through targeted evaluation.
Solution Approach 2:
The system performs partial action by selectively augmenting only the specific set of tables identified as targets based on graph node classification, rather than comprehensively evaluating and potentially augmenting all database tables. The target set determination mechanism ensures that sufficient tables are augmented to maintain database consistency without the excessive time cost of examining every table in the database environment.
Data Source
AI summary
Methods and apparatus are disclosed for supporting the addition of new instances of database objects and relationships, for a relational database environment that can be partially represented as a graph of fields (columns) of database tables. For a new instance of a graph element (node or edge) to be spawned, the element is classified into one of several use cases. Each database table is analyzed based on its key and non-key fields, according to use-case-dependent rules, to determine whether it should be augmented with a new entry for the new element instance. If yes, then the database table can be augmented, or can be marked for augmentation upon a subsequent request for a new instance of the graph element. Efficient and reliable growth of large and complex relational databases is enabled. Examples are provided across a wide range of domains. Certain variants are also disclosed.