Business Entity Identifier Matching for Automatic User Data Discovery
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Solution Overview
Problem
The manual annotation method for discovering user data in compliance governance projects is resource-intensive and suffers from low accuracy due to business changes.
Innovation Solution
A data discovery method that involves acquiring a data sample from a target business asset object, detecting segments conforming to a business entity identifier format, and invoking business entity services to determine user data presence based on these segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation method is used to discover user data, then human resources can be directly applied to identify data, but the accuracy rate is low and resource consumption is high
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated system that uses format detection rules and regular expressions to identify user data. The system automatically detects data segments matching predefined formats (e.g., phone numbers, IDs) without human intervention, thereby improving accuracy while reducing resource consumption.
Solution Approach 2:
The system enables self-service by allowing data to be automatically classified and identified through predefined format rules. The detection mechanism autonomously processes data samples, matches them against known formats, and identifies user data without requiring continuous human oversight or manual annotation efforts.
2Measurement precision
If manual annotation method is used, then flexibility in handling diverse data formats is maintained, but accuracy decreases due to business changes
Solution Approach 1:
The patent implements a dynamic rule management system where format detection rules can be automatically updated and adjusted based on business changes. The system maintains adaptability by allowing rules to be modified without requiring complete system redesign, enabling continuous improvement of detection accuracy as business requirements evolve.
Solution Approach 2:
The system handles diverse data formats by changing detection parameters and format patterns according to specific business contexts. Different format rules can be applied based on data type, business domain, and regulatory requirements, allowing the system to adapt to various data structures while maintaining high accuracy through parameterized detection logic.
Data Source
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AI summary
The present disclosure provides a data discovery method, apparatus, and device, and a storage medium. The method includes: first acquiring a data sample to be detected from a target business asset object, and then detecting whether there is a data segment in the data sample to be detected that conforms to a business entity identifier format; if it is determined that there is a data segment in the data sample to be detected that conforms to the business entity identifier format, invoking at least one business entity service to determine whether user data can be acquired based on the data segment; and if it is determined that the user data has been acquired based on the data segment, determining that the target business asset object stores the user data. Apparently, based on a relationship between a business entity identifier and the user data, this embodiment of the present disclosure achieves automatic discovery of the user data by determining whether there is the business entity identifier in the target business asset object, thereby improving the accuracy of user data discovery.