Graph Data Quality Verification for Database Import
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional databases face challenges in handling complex relational operations between massive data, leading to inefficiencies when importing data into graph databases, and there is a need for ensuring data quality within these databases.
Innovation Solution
A method and device for graph data quality verification that involves comparing table structure information, determining if the data meets a list fusion condition, fusing the data, detecting outliers, and generating a quality report to ensure accurate import and quality of graph data into a graph database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If source data is imported directly into the graph database, then the import process is simple and fast, but the data quality cannot be ensured and repeated cleaning is needed
Solution Approach 1:
The patent performs data quality verification before importing data into the graph database. It compares table structure information, detects outliers in graph data nodes and edges, and identifies data quality issues in advance. This preliminary verification ensures data quality while maintaining import efficiency by preventing the need for repeated cleaning operations.
2Reliability
If data verification is performed before import, then data quality is improved, but the processing time and complexity increase
Solution Approach 1:
The patent segments the data verification process into distinct modules: table structure information comparison, graph data fusion, outlier detection for nodes and edges, and quality report generation. Each module handles a specific aspect of verification, making the overall process more efficient and manageable while ensuring comprehensive data quality checking.
3Ease of operation
If traditional databases are used for relational operations, then the system is simple to operate, but it cannot efficiently handle massive and complex data relationships
Solution Approach 1:
The patent changes the data structure parameter from traditional tabular databases to graph database structure with nodes and edges. This parameter change enables efficient handling of complex relational operations on massive data while maintaining ease of operation through automated verification processes that mask the underlying complexity.
Data Source
AI summary
Disclosed are a method and a device for graph data quality verification, which can perform quality verification of the graph data to be processed before importing the graph data to be processed to the target graph database, thereby avoiding generating a target list based on the graph data to be processed with errors. By determining whether there is an outlier in the target list, the abnormal graph data in the graph data to be processed can be detected to ensure the correctness of the graph data to be processed imported into the target graph database. By generating a graph data quality report, it is possible to verify whether the graph data to be processed has errors during the import process.