Effective Tag Determination for Data Assets
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Solution Overview
Problem
The complexity of managing and tracking data assets due to increased governmental regulations, such as GDPR, leads to conflicting and inaccurate data classifications, rendering existing classification schemes ineffective.
Innovation Solution
A system that determines an effective tag for data assets by analyzing attributes like confidence level, applied date, and hierarchical depth of the tags associated with the data assets, providing a simplified and accurate classification by retrieving and analyzing all tags applied to a data asset and returning the effective tag to the requesting entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple classification processes are used to manage data assets under governmental regulations, then data tracking and compliance capability is improved, but classification accuracy and reliability deteriorate due to conflicting classifications
Solution Approach 1:
The patent introduces an intermediary resolution mechanism that receives conflicting classifications from multiple processes, analyzes their attributes (confidence levels, sources, timestamps), and determines a single effective classification. This mediator resolves conflicts between different classification processes, allowing the system to maintain multiple tracking capabilities while ensuring reliable, accurate classifications through reasoned selection among competing options.
2Reliability
If all tags and their attributes are retrieved and analyzed for every data asset, then classification accuracy is improved, but computing time and processing resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-establishing attribute weights and resolution rules before actual classification requests occur. The system pre-organizes tag attributes (confidence levels, sources, timestamps) in a structured format, so when a classification request arrives, the resolution process can immediately apply pre-defined criteria rather than analyzing everything from scratch. This preliminary preparation significantly reduces real-time processing time while maintaining accurate classification through comprehensive attribute analysis.
3Measurement precision
If detailed attribute analysis of tags is performed, then effective tag determination accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent segments the tag attribute analysis into distinct, manageable components: confidence level evaluation, source verification, timestamp comparison, and hierarchical depth assessment. Each attribute type is processed separately using dedicated resolution rules, making the overall complex determination process modular and more manageable. This segmentation allows the system to achieve precise effective tag determination by systematically evaluating each attribute dimension independently, reducing the cognitive load and processing complexity compared to a monolithic analysis approach.
Data Source
AI summary
Techniques described herein are directed to determining an effective tag for data assets. For instance, each tag associated with a data asset may be associated with certain attributes. Non-limiting examples for such attributes may include a confidence level associated with a source that applied the tag, an applied date at which the tag was applied to the data asset, and/or a hierarchical depth of the data asset to which the tag was applied, as well as additional and/or alternative types of attributes. When a request to determine a tag for a data asset is received, the attributes for all the tags applied to the data asset may be retrieved and/or analyzed to determine which of such tags effectively classifies the data asset. The determined effective tag may be returned to the requesting entity.