Data Model Optimization Using Column Call Patterns and Similarity
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Solution Overview
Problem
Existing technologies fail to regenerate a conversion formula based on evaluation of JSON format, making it impossible to generate a data model suitable for applications by considering consolidation and division of database tables.
Innovation Solution
A data model optimization system that includes a single call count calculation unit, a set call count calculation unit, and a similarity degree calculation unit to generate a data model suitable for applications by evaluating database configurations and data acquisition scenarios, using a structure optimized for data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a data model is generated without evaluating conversion formulas, then the generation process is simple, but the data model is not suitable for application requirements
Solution Approach 1:
The system evaluates conversion formulas by calculating call counts and similarity degrees, then uses this feedback to regenerate optimized conversion formulas. This closed-loop feedback mechanism ensures the data model becomes suitable for application requirements while maintaining automated optimization.
Solution Approach 2:
The system automatically evaluates and regenerates conversion formulas without manual intervention. The automated evaluation process calculates call counts, determines similarity degrees, and performs regeneration independently, making the system self-sufficient in optimizing data models for application suitability.
2Device complexity
If database tables are not consolidated or divided, then the database structure is simple, but communication efficiency is poor
Solution Approach 1:
The system divides database tables into separate tables based on evaluation results showing low similarity degrees and high call counts. This segmentation improves communication efficiency by organizing data access patterns into dedicated tables, reducing redundant queries while maintaining manageable structural complexity.
Solution Approach 2:
The system consolidates tables by merging those with high similarity degrees and low call counts. This merging improves communication efficiency by reducing the number of separate table accesses needed, optimizing data retrieval operations without creating excessive structural complexity.
3Loss of time
If conversion formulas are not regenerated based on evaluation, then processing time is short, but the number of database inquiries increases
Solution Approach 1:
The system performs preliminary evaluation of conversion formulas by calculating call counts and similarity degrees before executing data acquisition. This preliminary analysis enables the regeneration of optimized conversion formulas that reduce the number of database inquiries, and the optimized formulas are then applied in subsequent operations.
Data Source
AI summary
A calculation unit (110) calculates for each column in a plurality of tables, a call count which is the number of times that the column is called when data is acquired according to a data acquisition scenario, based on database configuration information. The calculation unit (110) calculates for each column set in the plurality of tables, a call count which is the number of times that the column set is called at the same timing when data is acquired from a database according to the data acquisition scenario. The calculation unit (110) calculates for each column set in the plurality of tables, a similarity degree between names of columns. A generation unit (130) generates a data model based on the call count for each column, the call count for each column set, and the similarity degree for each column set.