Data Redundancy Management System Using Machine Learning Analysis
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Solution Overview
Problem
Enterprise organizations face inefficiencies in storing vast amounts of data, often leading to redundant data storage that requires additional resources and is difficult to evaluate for value and relevance.
Innovation Solution
A data relation and redundancy management system using machine learning to analyze data from multiple sources, identify types, connections, and redundancies, and execute actions such as compression or deletion based on predetermined rules to optimize storage capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is continuously stored from multiple sources, then data completeness is improved, but storage efficiency deteriorates due to redundant data
Solution Approach 1:
The system performs preliminary analysis of incoming data to identify redundancies before storage. The data relation engine analyzes relationships between new data and existing data, and the redundancy engine identifies duplicate or superseded data, preventing redundant storage in the first place
Solution Approach 2:
The system changes the state of redundant data by transforming it into compressed or archived formats. The redundancy engine applies compression techniques to identified redundant data, converting it from a storage burden into a space-efficient representation that preserves data completeness while reducing storage requirements
2Adaptability or versatility
If all data is retained without evaluation, then data availability is improved, but resource allocation deteriorates
Solution Approach 1:
The system performs preliminary evaluation of data value and relevance before determining retention decisions. The redundancy engine proactively identifies outdated or superseded data and applies retention policies, ensuring that only valuable data is retained while freeing resources for future data needs
Solution Approach 2:
The system selectively discards redundant data that no longer provides value while recovering storage resources. The redundancy engine identifies data eligible for deletion based on retention policies and relationships, and the system safely removes this data, recovering storage resources for more valuable data while maintaining availability of essential information
3Volume of stationary object
If data redundancy actions are taken without analysis, then storage space is improved, but data integrity deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the data relation engine continuously monitors data relationships and the redundancy engine receives feedback about data value and usage. This feedback loop ensures that redundancy actions are based on current data states and relationships, preventing premature or incorrect deletion while optimizing storage space
Solution Approach 2:
The system performs preliminary analysis of data relationships and value before applying redundancy actions. The redundancy engine evaluates data importance, checks for superseded relationships, and verifies retention policy compliance before compression or deletion, ensuring data integrity is maintained while storage space is optimized
Data Source
AI summary
Arrangements for controlling data relations and data redundancies are presented. In some aspects, data may be received from a plurality of sources. The data may then be analyzed to determine a score or value associated with the data. Machine learning may be used to analyze the data and/or determine the score associated with the data. In some examples, a type of data may be identified. Based on the type of data, and the data, one or more connections to other data or types of data may be identified. In some examples, the data, as well as any identified connections, any determined score, and the like may be stored in a data container associated with the identified type of data. Stored data may be analyzed to determine whether one or more redundancies exist. If one or more redundancies are identified, one or more data redundancy actions may be identified and executed.