Recursive Linked-Data Deletion Across Distributed Workspaces
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Solution Overview
Problem
Conventional data management systems struggle with the efficient deletion of data and its related references due to the complexity of data relationships and dependencies, especially in large distributed systems, leading to incomplete data removal and resource inefficiencies.
Innovation Solution
A methodical process involving recursive traversal and endpoint identification is employed to identify and delete seed data and its linked data, using search criteria and an endpoint process to flag data for deletion, with automatic monitoring and notification features to ensure comprehensive data removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If data are deleted from a dataset, then storage space is reclaimed, but referenced data and derived objects remain orphaned causing data integrity issues
Solution Approach 1:
The system performs preliminary actions by identifying all referenced data and derived objects before deletion occurs. The traversal process systematically finds all data that reference the target data and all derived objects, marking them for deletion in advance. This ensures that when deletion happens, data integrity is maintained because all dependent data are removed together.
Solution Approach 2:
The deletion process is segmented into distinct phases: identification of target data, traversal to find referenced data and derived objects, marking for deletion, and execution. This segmentation allows the system to handle the complex deletion task in manageable steps, ensuring that no referenced data is left orphaned while efficiently reclaiming storage space.
2Productivity
If a simple delete operation is performed, then deletion speed is fast, but incomplete data removal occurs leaving orphaned references
Solution Approach 1:
The system maintains continuous useful action by implementing an automated traversal and deletion process that continuously identifies and removes all related data. Rather than performing a single static delete operation, the system continuously traverses data relationships and ensures complete removal of all referenced data and derived objects, preventing orphaned references while maintaining efficient deletion.
Solution Approach 2:
The deletion system performs self-service by automatically traversing data relationships and identifying all data that should be deleted. The system independently finds referenced data, derived objects, and marks them for deletion without requiring manual intervention, ensuring complete data removal while maintaining deletion efficiency.
3Ease of operation
If manual deletion processes are used, then control over deletion is high, but time consumption and labor increase significantly
Solution Approach 1:
The system implements self-service by automatically traversing data relationships, identifying target data, finding all referenced data and derived objects, and executing deletion without manual intervention. This automation maintains high control over the deletion process through programmable logic while dramatically reducing the time and labor required compared to manual deletion methods.
Solution Approach 2:
The system performs preliminary actions by pre-identifying all data that should be deleted through automated traversal of data relationships. This preliminary identification and marking process allows the actual deletion to execute quickly and efficiently, reducing overall deletion time while maintaining precise control over what data is removed.
4Reliability
If comprehensive data traversal is performed to ensure complete deletion, then data integrity is maintained, but system performance and processing time decrease
Solution Approach 1:
The system performs preliminary traversal and identification of all data relationships before deletion execution. By pre-marking data for deletion through systematic traversal, the actual deletion process becomes faster and more efficient. This preliminary action ensures data integrity through comprehensive identification while improving overall system performance by separating the intensive traversal phase from the execution phase.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media are provided for recursively searching a plurality of workspaces of the system for linked data associated with the seed data, initiating an endpoint process for each the seed data and the linked data, and, upon completion of the search, delete the seed data and the linked data identified based at least in part on the endpoint process. The process may be automatically repeated at a predetermined time interval to identify and remove future data that is stored in the plurality of datasets.