Modular Data Analysis Schema for Automated Consistency Checks
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Solution Overview
Problem
Existing data analysis methods are labor-intensive and often specific to particular scenarios, making it difficult to detect and address data inconsistencies across multiple data storage systems, which can lead to negative impacts on users and companies.
Innovation Solution
A modular data analysis schema that allows users to combine and modify data sources, check methods, and data evaluators, enabling the creation of reusable analysis procedures and facilitating routine checks for data consistency, thereby reducing the time and expertise required for data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data analysis reports are created for specific circumstances, then data consistency can be checked, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent segments data analysis into reusable modular components including data sources, check methods, and data evaluators. Each component can be independently developed, stored in libraries, and combined to form comprehensive analysis schemas, eliminating the need to create entire analysis reports from scratch for each new scenario.
Solution Approach 2:
The patent enables copying and reusing of analysis schemas, data sources, check methods, and evaluators across different scenarios. Once a data analysis component is created, it can be replicated and adapted for multiple uses, significantly reducing the time and effort required for subsequent data consistency checks.
2Reliability
If custom data analysis reports are developed for specific scenarios, then data consistency can be detected, but the complexity and expertise required increases
Solution Approach 1:
The patent creates universal data analysis components that can serve multiple functions and scenarios. Data sources, check methods, and evaluators are designed to be reusable across different data consistency checking scenarios, reducing the need for scenario-specific custom development and lowering the expertise barrier.
Solution Approach 2:
The patent employs a schema-based approach where analysis configurations can be quickly created, modified, and discarded without significant investment. The modular component library allows for rapid assembly of analysis schemas without requiring deep technical expertise, making the system more accessible to users with varying skill levels.
3Reliability
If comprehensive data analysis is performed across multiple data storage systems, then data inconsistencies can be detected, but the labor and resources required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining data sources, check methods, and evaluators in reusable libraries before actual data analysis is needed. These pre-configured components can be quickly assembled into analysis schemas, eliminating the need to perform complex analysis setup work at the time of data consistency checking.
Solution Approach 2:
The patent enables discarding of incomplete or unsuccessful analysis schemas and recovering of successful patterns for future use. The component library accumulates reusable elements from previous analyses, allowing the system to learn from past experiences and improve efficiency over time without repeating the same labor-intensive setup work.
Data Source
AI summary
An identifier of a data source specifying data to be analyzed is determined from a data analysis schema. The data source is called, and data to be analyzed is retrieved. From the data analysis schema, an identifier of a check method to be used to analyze the data is determined. The check method is called and used to analyze the data.