Retention-Policy Data Deletion with Selective Dataset Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data deletion methods are inefficient and risky, often leading to accidental deletion of important data and excessive computational demands, which can cause system failure.
Innovation Solution
A method involving a retention policy expressed in policy expression language code to selectively delete data elements, using dataset and transaction selectors, with mechanisms for parallel deletion and reattempting failed deletions to ensure data integrity and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual deletion methods are used, then data can be deleted, but the risk of deleting important data increases
Solution Approach 1:
The system performs preliminary evaluation of datasets against retention policies before deletion to identify deletable data elements. This preliminary action ensures that only data meeting deletion criteria is selected, preventing accidental deletion of important data while enabling automated safe deletion operations
Solution Approach 2:
The system implements feedback mechanisms where deletion operations are monitored and evaluated. The policy evaluation framework provides feedback on which data elements qualify for deletion based on retention policies, ensuring that deletion decisions are verified and reversible if errors are detected
2Quantity of substance
If large quantities of data are deleted using manual methods, then data storage is reduced, but substantial computational demands are made on the system
Solution Approach 1:
The system segments the data deletion process into distinct phases: policy evaluation, dataset identification, element selection, and deletion execution. This segmentation allows computational resources to be distributed across multiple processing stages, reducing peak resource consumption while handling large volumes of data through systematic breakdown of the deletion task
Solution Approach 2:
The system performs preliminary policy evaluation and data element identification before actual deletion operations. This preliminary action pre-sorts and pre-identifies deletable data elements, so that the actual deletion phase requires minimal computational overhead, enabling efficient processing of large data volumes
3Productivity
If automated deletion is implemented, then deletion efficiency improves, but the complexity of the deletion system increases
Solution Approach 1:
The system implements a universal policy evaluation framework that handles multiple datasets, retention policies, and deletion criteria through a single automated mechanism. This multi-functional approach consolidates what would otherwise require multiple separate deletion systems, achieving high deletion efficiency while managing complexity through unified policy-based control
Data Source
AI summary
Apparatus and methods receive input descriptive of a retention policy; evaluate one or more datasets against the retention policy to determine one or more deletable data elements in the one or more datasets; and delete the one or more deletable data elements from a data store.