Digital Data Valuation Using Crawlers for Redundant Storage Risk
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Solution Overview
Problem
The challenge of distinguishing valuable from less-valuable digital data in a data storage environment where important and unimportant data are stored together, and the same data is often duplicated, complicates data loss indemnification.
Innovation Solution
A data crawler identifies and classifies data based on characteristics such as rarity, frequency of access, level of protection, and originality, using machine learning models to assign monetary value and determine data loss likelihood, supported by fault tolerance and security configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in multiple locations simultaneously, then data redundancy and security are improved, but data valuation difficulty increases
Solution Approach 1:
The patent uses digital fingerprinting to create unique identifiers for data copies across multiple storage locations. Each copy receives a fingerprint that enables tracking and identification, allowing the system to recognize redundant data while maintaining security through distributed storage. This resolves the contradiction by enabling valuation of duplicated data without the complexity of manual tracking.
Solution Approach 2:
The system implements continuous monitoring and analysis of data storage patterns, access frequencies, and redundancy levels. By feeding this information back into the valuation model, the system dynamically adjusts valuations based on actual data performance and redundancy, simplifying the valuation process while maintaining accurate security assessments.
2Productivity
If valuable and less-valuable data are stored together, then storage efficiency is improved, but data classification accuracy deteriorates
Solution Approach 1:
The patent applies different valuation criteria and analysis methods to different types of data based on their characteristics. Rather than using a uniform approach, the system tailors its valuation process to specific data categories (personal, professional, media, etc.), maintaining high valuation accuracy while efficiently processing mixed storage environments.
Solution Approach 2:
The system segments data into distinct categories and applies targeted valuation methodologies to each segment. By dividing the heterogeneous data storage into manageable categories with specific valuation rules, the system maintains both storage efficiency and valuation accuracy without requiring complex analysis of every individual data element.
3Measurement precision
If data characteristics are analyzed in detail, then data valuation precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of data characteristics and creates valuation profiles in advance. By pre-processing data metadata, access patterns, and storage information before formal valuation is needed, the system reduces the time required for detailed analysis while maintaining high valuation precision through pre-established data frameworks.
Data Source
AI summary
Techniques provided herein employ a data crawler to sort through data stored on an electronic device to enable classification of data based on certain characteristics. For example, the data crawler can assess data characteristics such as rarity, originality, frequency of access, level of customization and level of security to help determine the value of data for indemnification purposes. Such characterizations may be used alongside a device security assessment to determine the data loss insurance premium and/or coverage.