Automated Data Model Generation Service for Database Compliance
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Solution Overview
Problem
Manual data model updates in database systems are prone to human error and delays, making it difficult for organizations to comply with data protection and privacy regulations like GDPR, which require accurate and timely retrieval and deletion of personal data.
Innovation Solution
A model generation service that automatically builds and updates data models in real-time by identifying relevant database tables and fields, using machine learning to predict links between tables and filter non-relevant data, thus reducing human intervention and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates of data models are performed by human operators, then the data model can be adjusted to reflect changes in data storage, but human errors and delays occur leading to incorrect or outdated data models
Solution Approach 1:
The system enables self-service by automatically generating and updating data models through the model generation service that monitors database changes and produces updated models without human intervention, eliminating both human error and delays
Solution Approach 2:
The model generation service performs preliminary actions by continuously monitoring database changes and preparing updated data models in advance, so that when compliance requests occur, accurate and current data models are already available
2Adaptability or versatility
If manual adjustment of data models is performed, then flexibility in adapting to data storage changes is achieved, but multiple sources of errors are introduced
Solution Approach 1:
The patent replaces the mechanical system of manual human adjustment with an automated computational system that uses machine learning models to monitor database changes and generate updated data models, thereby maintaining adaptability while eliminating human error
Solution Approach 2:
The system implements feedback by continuously monitoring database changes and using this information to automatically update data models, creating a closed-loop system that adapts to changes while maintaining high reliability through automated validation
3Productivity
If ILM objects are used to generate data models, then data lifecycle management is enabled, but errors in ILM objects propagate to data models
Solution Approach 1:
The model generation service acts as an intermediary between ILM objects and data models, monitoring changes in ILM objects and using machine learning to generate accurate data models that reflect current database state, preventing error propagation while maintaining productivity
Data Source
AI summary
Provided is a system and method for generating and updating a data model for use in retrieving data from an information retrieval system such as a database, a server, and the like. In one example, the method may include monitoring data that is written to database tables of a database by a software process, identifying links between the database tables where the monitored data is written, determining whether the software process has a pre-existing data model for retrieving data stored in the database, and in response to a determination that the software process does not include the pre-existing data model, creating a new data model for the software process which includes names of the database tables where the monitored data is written and links between the database tables, and storing the new data model via a database repository.