Storage Apparatus Regeneratable Data Analysis Management
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Solution Overview
Problem
Current data storage solutions for big data analysis, such as cloud services, face high costs due to increasing data capacity, and existing techniques like Information Lifecycle Management (ILM) are unclear about data regenerability, making it difficult to determine whether data can be deleted.
Innovation Solution
A storage apparatus that selects and deletes analysis data based on regeneratable attribute information, allowing for selective deletion of data that can be restored, thereby reducing storage needs and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is deleted to reduce storage capacity, then storage costs are reduced, but the risk of deleting irrecoverable data increases
Solution Approach 1:
The system performs preliminary classification of data into regeneratable and non-regeneratable categories before deletion occurs. By pre-tagging data with regeneration capability attributes and establishing classification rules in advance, the system ensures that only data confirmed to be regeneratable is deleted, eliminating the risk of irreversible data loss while maximizing storage space reclamation.
2Reliability
If all data is retained to ensure no data loss, then data recoverability is maintained, but storage costs increase
Solution Approach 1:
The system applies different retention policies to different data based on their regeneratable attributes. Regeneratable data is deleted to free storage space, while non-regeneratable data is retained with higher priority. This localized quality approach ensures that each data item receives appropriate treatment based on its specific characteristics, optimizing the balance between storage efficiency and data recoverability.
3Productivity
If data classification is performed to enable selective deletion, then storage efficiency is improved, but system complexity increases
Solution Approach 1:
The system enables data to self-identify its regeneratable status through automatic tagging during data ingestion and processing. Data items carry inherent attributes that indicate their regeneration capability, allowing the system to automatically classify and manage them without complex external intervention. This self-service mechanism simplifies the overall system architecture while maintaining high storage efficiency.
Data Source
AI summary
A storage apparatus and data management method capable of utilizing storage resources effectively is proposed. A storage apparatus storing primary data and analysis data obtained based on the primary data as a result of the execution of specified analysis processing by an external computing system is designed so that metadata of the analysis data includes regeneratable attribute information indicating whether or not the corresponding analysis data can be regenerated by means of the analysis processing by the external computing system; and a control unit regularly or irregularly selects the analysis data, which satisfies a specified condition and can be regenerated, based on the metadata for each piece of the analysis data and deletes the selected analysis data from one or more storage devices.