File Archiving Based on Data Relevance Attributes
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Solution Overview
Problem
Current data archiving systems lack the ability to differentiate between critical and non-critical data objects, leading to inefficient storage management, where business-critical documents may be archived on low-cost, low-performance storage, resulting in wasted time and potential information loss.
Innovation Solution
A system and method that assign a relevance attribute to data objects based on their importance, allowing for archiving to high-end or low-end storage tiers, with the option to dynamically determine relevance values using analytics engines and user-defined parameters, ensuring critical data is stored on high-end storage for performance and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is archived to remote storage to increase local storage capacity, then storage resources are alleviated, but the system cannot differentiate between critical and non-critical data objects, resulting in arbitrary archiving priorities
Solution Approach 1:
The patent assigns different relevance values to different data objects based on their criticality, creating local quality differentiation. Critical data objects receive higher relevance values and are archived to high-end storage, while non-critical data objects receive lower relevance values and are archived to low-end storage, resolving the contradiction between storage capacity and data criticality preservation
Solution Approach 2:
The patent introduces a relevance value parameter that changes the archiving behavior of the system. By calculating and assigning relevance values based on user behavior patterns, file types, and access frequencies, the system dynamically adjusts archiving priorities to preserve critical data while maximizing storage utilization
2Loss of energy
If critical data objects are archived on low-cost, low-performance storage, then storage costs are reduced, but access time increases and information may be lost
Solution Approach 1:
The patent creates quality differentiation in storage allocation by assigning critical data objects to high-performance storage and non-critical data objects to low-performance storage based on relevance values, ensuring that time-sensitive operations access data from high-performance storage while reducing overall storage costs
Solution Approach 2:
The patent implements a feedback mechanism that monitors user access patterns and dynamically adjusts relevance values. When critical data is accessed, the system learns from this behavior and ensures future archiving decisions prioritize quick access to frequently accessed data, reducing information loss and access time
3Device complexity
If all data objects are treated equally in archiving, then the archiving process is simple, but the system cannot adapt to changing user priorities and may arbitrarily archive critical documents
Solution Approach 1:
The patent implements self-service archiving where the system automatically calculates relevance values based on user behavior patterns, file metadata, and access frequencies. Users can optionally override automatic assignments, but the system handles the complex differentiation task autonomously, adapting to changing priorities without increasing operational complexity
Solution Approach 2:
The patent introduces dynamic relevance values that change over time based on user behavior patterns and access frequencies. Data objects that become more frequently accessed automatically receive higher relevance values, allowing the system to adapt to changing user priorities dynamically without manual intervention
Data Source
AI summary
A method and system for archiving data based on a defined relevance attribute is discussed. This attribute may be based on the data's importance to a business or user. In an embodiment, more important data may be placed in high-end storage and less critical data may be placed in low-end storage.