Graph Rule Normalization Engine for Data Quality
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Solution Overview
Problem
In resource management, data integration in databases is often inconsistent and incomplete, leading to complex connection maintenance issues and errors that impact data quality and applications utilizing this data, necessitating improved data quality through effective connection maintenance.
Innovation Solution
A computer system with a normalization engine that defines and applies graph rules to entities and relationships, identifying discrepancies and performing actions to update and correct them, ensuring clean graph models and improved data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual browsing and correction of connection errors is performed, then connection maintenance can be achieved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service through automatic detection and correction of connection errors. The normalization engine autonomously identifies discrepancies in CI relationships and applies corrections without requiring manual intervention, allowing the system to maintain and improve itself automatically.
Solution Approach 2:
Manual mechanical browsing and correction processes are replaced with an automated computational system. The normalization engine uses algorithmic rules to detect and correct connection errors, substituting human labor with an automated information processing system that operates continuously and efficiently.
2Quantity of substance
If data integration is performed from multiple discovery providers, then data completeness can be improved, but data consistency and quality deteriorate due to integration errors
Solution Approach 1:
The system changes the state of integrated data by applying normalization rules that modify inconsistent parameters. The normalization engine detects parameter discrepancies across data from multiple sources and transforms them into consistent states, ensuring data quality while maintaining completeness.
Solution Approach 2:
The system implements feedback mechanisms where the normalization engine continuously monitors integrated data for consistency errors and automatically applies corrections. This closed-loop feedback process ensures that data from multiple discovery providers maintains both completeness and consistency through ongoing validation and correction.
3Reliability
If graph rules are applied to validate entities and relationships, then data quality improves, but processing complexity increases
Solution Approach 1:
The normalization engine is segmented into distinct functional modules: rule definition module for creating validation rules, rule application module for executing rules against data, and rule action module for implementing corrections. This segmentation reduces overall system complexity by breaking down the validation process into manageable, independent components.
Data Source
AI summary
In accordance with aspects of the disclosure, systems and methods are provided for normalizing data representing entities and relationships linking the entities including defining one or more graph rules describing searchable characteristics for the data representing the entities and relationships linking the entities, applying the one or more graph rules to the data representing the entities and the relationships linking the entities, identifying one or more matching instances between the one or more graph rules and the data representing the entities and the relationships linking the entities, and performing one or more actions to update the one or more matching instances between the one or more graph rules and the data representing the entities and the relationships linking the entities.