Attribute Table Generation for Customer Data Exemption
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Solution Overview
Problem
Existing database systems face challenges in generating attribute tables dynamically during business plan approval processes and aggregating customer profile data while also needing to exclude or hide specific information, especially in large datasets from online transactions.
Innovation Solution
The system automatically generates attribute tables and workflows for aggregating customer profile data, excluding irrelevant information, and creating buying patterns based on user inputs, using a computer-assisted retrieval engine to process and store attribute information in real-time, allowing for flexible database management and business decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attribute tables are generated manually for each business plan, then data accuracy and relevance are improved, but development time and manual effort increase significantly
Solution Approach 1:
The system pre-generates attribute tables and data aggregation workflows automatically before business plans are fully approved. By performing data table creation in advance based on preliminary business plan parameters, the system eliminates manual intervention during the approval process, reducing development time while maintaining data accuracy through automated validation rules.
Solution Approach 2:
The system enables automatic self-service generation of attribute tables and data aggregation workflows. The automated system uses business plan parameters to dynamically create appropriate data structures and aggregation logic without requiring manual configuration, thereby reducing both development time and manual effort while maintaining data accuracy through consistent automated processes.
2Adaptability or versatility
If all customer profile data is aggregated and stored, then comprehensive analysis capability is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system applies local quality by creating customized attribute tables and data aggregation workflows specific to each business plan's requirements. Rather than aggregating all possible customer profile data uniformly, the system selectively aggregates only the relevant data attributes needed for each specific analysis scenario, reducing processing complexity while maintaining comprehensive analysis capability for each use case.
Solution Approach 2:
The system segments customer profile data aggregation into multiple specialized attribute tables, each tailored to specific business plan requirements. By dividing the data aggregation process into separate, purpose-specific tables rather than one monolithic structure, the system reduces processing complexity while enabling comprehensive analysis across different business contexts.
3Loss of information
If sensitive customer information is included in aggregated data, then data completeness is improved, but customer privacy and data security risks increase
Solution Approach 1:
The system extracts and separates sensitive customer information from the aggregated data sets used for business plan analysis. By removing personally identifiable information and sensitive details from the attribute tables while retaining necessary analytical data, the system maintains data completeness for business purposes while eliminating privacy and security risks associated with storing sensitive customer information.
4Adaptability or versatility
If data tables are created manually for each business plan, then customization and relevance are improved, but scalability and automation capability decrease
Solution Approach 1:
The system dynamically generates attribute tables and data aggregation workflows automatically based on business plan parameters. Rather than requiring manual creation for each plan, the system adapts to different business requirements through automated configuration, maintaining high customization and relevance while achieving full automation capability and scalability.
Solution Approach 2:
The system creates a universal automated framework that can generate customized attribute tables and aggregation workflows for any business plan type. This multi-functional system handles diverse business requirements through a single automated process, achieving both high customization capability and full automation, thereby improving scalability without sacrificing adaptability.
Data Source
AI summary
The present disclosure extends to methods, systems, and computer program products for generating attribute tables for holding attributes while a corresponding business plan is in an approval process. The present disclosure also extends to methods, systems and computer program products for approving proposed business plans and automatically generating workflow for establishing data tables for aggregating customer profile data in those data tables. The present disclosure also extends to methods, systems and computer programs for excluding, hiding or removing certain items of information or data from data contained in one or more attribute tables.