Medoid-Based Data Compression for Storage Efficiency
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Solution Overview
Problem
The increasing volume of electronic data poses significant storage challenges, as traditional file storage methods require substantial resources and incur high costs, necessitating the development of efficient data compression techniques.
Innovation Solution
The method involves determining medoids for itemized data through clustering algorithms, encoding items, identifying differences between items and their corresponding medoids, and storing these differences, which significantly reduces storage requirements by minimizing the space needed for each item.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional file storage methods are used to store electronic data, then data can be stored and accessed, but storage capacity requirements and costs increase significantly
Solution Approach 1:
The patent segments data into clusters based on similarity, where each cluster is represented by a medoid. Instead of storing complete data items, only medoids and difference vectors are stored. This segmentation reduces storage requirements while maintaining data accessibility through reconstruction from medoids and differences.
Solution Approach 2:
The patent creates simplified copies of data in the form of difference vectors that represent only the variations from medoids. These difference vectors are much smaller than original data items, yet when combined with medoids, they reconstruct the original data, thus reducing storage capacity while preserving data integrity.
2Reliability
If complete data items are stored for each item, then data integrity is maintained, but storage costs and physical resources increase
Solution Approach 1:
The patent extracts the essential representative information from each data cluster by computing medoids and difference vectors. Only the medoid and the difference vector are stored, extracting the minimum necessary information to reconstruct original data, thereby reducing storage costs while maintaining data integrity through lossless reconstruction.
3Quantity of substance
If data is compressed by storing medoids and differences, then storage requirements are reduced, but processing complexity increases
Solution Approach 1:
The patent performs preliminary clustering and medoid computation during the data compression phase. By pre-organizing data into clusters and computing medoids beforehand, the system reduces the complexity of subsequent operations. When data needs to be accessed or processed, the pre-computed medoids and difference vectors enable efficient reconstruction without requiring complex real-time analysis.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for medoid-based data compression. One example method generally includes receiving item data indicative of one or more items, determining one or more medoids based on the item data, determining, for each item of the one or more items, a corresponding medoid based on the one or more medoids, identifying, for each item of the one or more items, a difference between the item and the corresponding medoid for the item, storing the one or more medoids, and storing, for each item of the one or more items, the identified difference between the item and the corresponding medoid.