Business Intelligence Table Operations for Flexible Data Analysis
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Solution Overview
Problem
Current business intelligence tools can only designate conditions based on attributes in master tables, limiting the presentation of information that may be helpful for decision-making.
Innovation Solution
A table operation system that accepts designation of foreign key columns, relation value columns, and statistical models, acquires total values, groups key values, and adds attribute value columns to tables, allowing for more flexible and analyzable information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a BI tool designates conditions based only on attributes in master tables, then the system structure remains simple, but the ability to present helpful information for decision-making is limited
Solution Approach 1:
The patent introduces a new dimension to the data model by adding synthesized columns that combine multiple existing columns (e.g., combining customer name, product name, and purchase date into a synthesized column). This dimensional expansion allows analysts to create complex analysis conditions without fundamentally changing the underlying simple table structure, thus improving adaptability while maintaining structural simplicity.
Solution Approach 2:
The patent introduces synthesized columns as intermediary elements between the simple master table structure and the complex analysis requirements. These synthesized columns act as mediators that pre-combine multiple attributes, allowing analysts to work with complex information while the underlying database structure remains simple and unchanged.
2Ease of operation
If the BI tool allows flexible condition designation, then analyzable information can be obtained, but the operation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-combining multiple columns into synthesized columns before the actual analysis operation. The system pre-calculates and stores combined values (such as concatenated strings of multiple attributes) in advance, so that during analysis operations, users can directly reference these pre-prepared synthesized columns without having to manually combine multiple fields, thus simplifying the operation process.
Solution Approach 2:
The system provides self-service functionality by automatically generating and maintaining synthesized columns based on the original table structures. The system itself performs the complex task of column combination and data synthesis, freeing users from having to manually configure complex table structures or write complex queries to combine multiple attributes.
3Adaptability or versatility
If multiple columns are combined into a synthesized column, then new analysis conditions can be created, but the table structure becomes more complex
Solution Approach 1:
The patent applies segmentation by separating the table structure into two distinct layers: the original master tables that maintain their simple, unchanged structure, and the synthesized columns that provide complex analysis capabilities. This segmentation allows the system to offer flexible analysis conditions through synthesized columns while keeping the core table structures simple and manageable.
Solution Approach 2:
The patent applies local quality by adding synthesized columns only where and when they are needed for specific analysis purposes, rather than fundamentally changing the entire table structure. The synthesized columns provide localized complexity enhancement in specific areas of the data model without affecting the overall simplicity of the database structure.
Data Source
AI summary
A data acquiring section specifies individual tables to serve as reference destinations of designated respective foreign key columns and, for each combination of key values in primary key columns of the specified individual tables, acquires a total value of relation values in a designated relation value column. A grouping section groups the respective key values in the primary key columns of the individual tables on the basis of the total value acquired for each combination of the key values in the primary key columns of the individual tables and a statistical model of a designated type. An attribute value column adding section adds, to each individual table, an attribute value column of which attribute value is identification information on a group to which each key value in each primary key column of each individual table belongs.