Graph Database Rules for Automated Unstructured Data Quality
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Solution Overview
Problem
The challenge of efficiently processing large volumes of unstructured data with low quality, which is difficult to analyze and requires additional resources, is exacerbated by the need for rapid data processing and low latency in applications like 5G networks, leading to potential inaccuracies and inconsistencies that can harm decision-making processes.
Innovation Solution
A system that utilizes a graph database to identify unstructured data, applies customized rules through assessment and rectifying code to improve data quality, and converts unstructured data to structured data for efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parsing and analysis of unstructured data is performed, then data quality can be assessed, but processing time increases and productivity decreases
Solution Approach 1:
The system employs automated assessment code that executes itself against unstructured data files, eliminating the need for manual parsing. The code automatically triggers when new data arrives, performs quality assessment using predefined rules, and generates reports without human intervention, thus maintaining measurement precision while dramatically improving productivity
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems. Assessment code runs automatically on unstructured data, using algorithmic rule evaluation instead of human analysts, which maintains assessment accuracy while enabling high-speed processing of large data volumes
2Productivity
If unstructured data is processed without automated quality detection, then processing speed is maintained, but data quality and reliability deteriorate
Solution Approach 1:
The system performs quality assessment as a preliminary action before data is fully processed or utilized. Assessment code triggers automatically when data arrives, evaluating quality against predefined rules before the data enters downstream processing pipelines, ensuring reliability is maintained without compromising processing speed
Solution Approach 2:
The system implements continuous feedback loops where assessment results are automatically generated and fed back into the data processing system. Quality metrics are computed and returned to stakeholders, enabling real-time monitoring and correction of data quality issues while maintaining high processing throughput
3Reliability
If manual assessment and modification of unstructured data is performed, then data quality can be improved, but resource consumption and costs increase
Solution Approach 1:
The assessment code operates autonomously, automatically triggering and executing quality assessments without requiring manual analyst time. The system self-manages the entire workflow from data ingestion to quality evaluation and reporting, improving data reliability while minimizing human resource consumption
Solution Approach 2:
The system allows dynamic adjustment of assessment rules and parameters based on data characteristics and business requirements. By optimizing rule complexity and assessment depth, the system maintains high data quality standards while adjusting resource consumption to match actual needs, preventing unnecessary processing overhead
Data Source
AI summary
This disclosure relates to assessment of data quality for unstructured data. In some aspects, a method includes obtaining, by one or more computing devices, metadata of multiple data files; analyzing a graph database representative of the multiple data files and generated using the metadata, to identify unstructured data included in one or more data files, the graph database representing features of the multiple data files, and relationships among the features of the multiple data files; obtaining a set of customized rules for the unstructured data based on context of the unstructured data; determining that the unstructured data fails to satisfy the set of customized rules; and in response to determining that the unstructured data fails to satisfy the set of customized rules, modifying the unstructured data to satisfy the set of customized rules.