Data Usage Tracking via Audit Tags and Segmentation
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Solution Overview
Problem
Current data management systems lack efficient mechanisms for managing license agreements between clients and local data systems, leading to challenges in ensuring compliance with data usage terms and tracking data usage effectively.
Innovation Solution
A method and system that involve a data management system, local data systems, and a data tracking service to manage license agreements, where audit tags are used to enforce compliance and track data usage, utilizing distributed transaction ledgers and smart contracts for secure data access and usage logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data management systems are used without audit tags and tracking services, then the system complexity is low, but data usage compliance cannot be effectively enforced and tracked
Solution Approach 1:
The system segments data management into distinct components: local data systems store and manage data sets, while a separate data tracking service monitors and enforces usage compliance. Audit tags are attached to data sets to track their usage independently, allowing compliance enforcement without centralizing all data management functions in one complex system.
Solution Approach 2:
Audit tags serve as intermediaries between data sets and the data tracking service. These tags carry identification information that enables the tracking service to monitor data usage without direct intervention in the core data management operations, thus enforcing compliance while maintaining system modularity.
2Measurement precision
If comprehensive data usage tracking is implemented, then compliance monitoring accuracy is improved, but the loss of time for data processing and tracking increases
Solution Approach 1:
Audit tags are attached to data sets in advance, containing identification information that enables immediate tracking upon data usage. This preliminary tagging eliminates the need for complex real-time analysis during data access operations, as the tracking service can directly query pre-tagged data using simple identification lookups.
Solution Approach 2:
The system uses identification copies (audit tags) rather than tracking the actual data content. Instead of monitoring and analyzing the full data sets, the tracking service works with compact tag information that replicates essential identification data, dramatically reducing processing time while maintaining tracking accuracy.
3Productivity
If audit tags are attached to all data sets for tracking, then data usage tracking capability is improved, but the quantity of data to be managed increases
Solution Approach 1:
The system extracts only the essential identification information from data sets and places it in separate audit tags. This extraction removes the bulk of data content from the tracking process, leaving only minimal tag information that references the original data sets. The data tracking service manages these lightweight tags rather than the full data sets themselves.
Solution Approach 2:
Audit tags act as intermediaries that reference data sets without containing their full content. Each tag holds a compact identifier that links to the original data set, allowing the tracking service to monitor usage of numerous data sets while managing only small tag structures, thus scaling tracking capability without proportionally increasing data volume.
Data Source
AI summary
In general, the invention relates to a method for managing data. The method includes obtaining a data set from a local data system, identifying an audit tag associated with the data set, generating a table entry for a data registration table based on the data set and the audit tag, and storing the table entry in the data registration table, wherein the data registration table is stored in a data tracking service.