Multilevel Entity Dependency Analytics for Database Schema Optimization
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Solution Overview
Problem
Legacy database tools lack the capability to perform multilevel entity dependency analytics, which are essential for optimizing database schema and addressing complex dependencies between parent and child entities, leading to inefficiencies in data model optimization and scalability issues.
Innovation Solution
A method and system that utilize multilevel entity dependency analytics by accessing a multilevel schema data structure, generating a dependency table, and performing high impact, referential integrity, and conformance analyses to optimize data models, including the identification of High Impact Entities, Circular Referential Integrity, Redundant Referential Integrity, and Entity Loading Sequence, while providing customizable and automated features for data modelers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy database tools are used, then basic database schema management is possible, but multilevel entity dependency analytics capability is lacking
Solution Approach 1:
The system segments the complex task of data model optimization into distinct analytical functions: high impact analysis, referential integrity analysis, and conformance analysis. Each function processes specific aspects of entity dependencies independently, allowing the system to handle multilevel dependencies without overwhelming complexity.
Solution Approach 2:
The patent introduces a dependency table as an intermediary data structure that captures relationship lineages between parent and child entities. This intermediary enables the system to analyze multilevel dependencies without directly complex interactions between all entities, simplifying the analytics process.
2Measurement precision
If detailed multilevel entity dependency analytics are implemented, then data model optimization capability is improved, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing relationship lineages in the dependency table before executing detailed analytics. This pre-processing step captures the structure of parent-child entity relationships, enabling faster execution of high impact, referential integrity, and conformance analyses without re-computing basic dependencies each time.
Solution Approach 2:
The patent applies partial action by allowing users to select specific analysis types (high impact, referential integrity, or conformance) based on their needs. Rather than always performing all three comprehensive analyses, the system executes only the necessary subset, reducing time consumption while maintaining precision where needed.
3Loss of information
If comprehensive analysis results are generated and reported, then user insight capability is improved, but information processing overhead increases
Solution Approach 1:
The system applies local quality by providing customized analysis results tailored to specific user needs and contexts. Rather than generating identical comprehensive reports for all users, the system can adjust the depth and focus of analysis output based on what is locally relevant to each user's data model optimization task.
Data Source
AI summary
A method, system, and computer program product for of database schema management. The computer implemented method for data model optimization using multilevel entity dependency analytics commences by accessing a multilevel schema data structure, determining the relationship lineages present in the multilevel schema data structure and generating a dependency table using the relationship lineage. Then, using the dependency table the computer implemented method performs at least one of, a high impact analysis, a referential integrity analysis, or a conformance analysis. In some embodiments the results of the analysis are reported to a user and in some embodiments the results of the analysis applied to the multilevel schema data structure.