Table Relation Analysis Grouping for Normalized Databases
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Solution Overview
Problem
In relational databases with a large number of tables, understanding the complex relationships between tables is challenging due to the lack of effective methods for organizing tables based on column occurrences, especially when normalization reduces the frequency of similar columns.
Innovation Solution
A table relation analysis assisting apparatus that consolidates tables with one-to-one and plural-to-one relationships into groups, using inter-table relation analysis information to visually display the relationships, facilitating intuitive grasping of data occurrences and reducing the complexity of table relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If tables are organized by column occurrence frequency (as in prior art), then similar tables can be consolidated, but this method becomes ineffective when normalization reduces the frequency of similar columns
Solution Approach 1:
The patent changes the organizational parameter from column occurrence frequency to data occurrence relationships between tables. Instead of counting how often similar columns appear, the system analyzes how data actually flows and relates between tables, making the method effective even in normalized databases where similar columns are rare.
Solution Approach 2:
Rather than organizing tables by their internal structure (columns), the patent inverts the approach by organizing tables based on their external relationships (data occurrences). This inversion allows the system to capture functional relationships that structural analysis misses in normalized databases.
2Loss of information
If all tables are displayed individually in relational databases, then complete information is available, but understanding complex relationships becomes difficult
Solution Approach 1:
The patent merges tables that have strong data occurrence relationships into table groups. By combining related tables into unified groups, the system maintains complete information while presenting a simplified view that highlights important relationships, making complex database structures easier to understand.
Solution Approach 2:
The patent segments the database view into table groups based on data relationships. Instead of presenting all tables as a single complex structure, the system divides them into meaningful segments or groups, allowing users to understand relationships at an appropriate level of abstraction while still accessing detailed information when needed.
3Ease of manufacture
If tables are grouped by hierarchical organization (as in prior art), then integration is facilitated, but the method cannot effectively utilize data occurrence relations in normalized databases
Solution Approach 1:
The patent changes the measurement parameter from structural similarity (column matching) to functional relationship (data occurrence). This allows precise identification of how tables actually interact in normalized databases, where structural similarity is reduced but functional relationships remain strong through foreign keys and data dependencies.
Data Source
AI summary
A table relation analysis assisting apparatus analyzes a relation between pieces of data in a certain column (external key) astride tables in a relational database. The apparatus analyzes a relation between tables and holds inter-table relation analysis information in which tables, pieces of data in the tables being in a one to one relation or a plural to one relation, are defined as one table group. In the apparatus, an analysis result display unit provides a display in such a form that one or more tables in a relational database are contracted into a table group. The display unit displays table groups in such a form that which of a one to one relation or a plural to one relation a relation between pieces of data included therein is astride tables can be viewed.