Data Block Matrix Compression for Storage Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage management systems do not effectively minimize or compress original datasets, leading to inefficient storage utilization and increased reference counts that reduce storage efficiency, despite techniques like deduplication and thin provisioning.
Innovation Solution
A method and system that convert data blocks into matrices for bit-level manipulation, compressing them using sequential compression rules to form minimum state matrices and derive granular metadata, which is then stored for efficient data compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deduplication and thin provisioning techniques are used to eliminate redundant datasets, then storage space utilization is improved, but the reference count of internal optimizer increases which reduces storage efficiency
Solution Approach 1:
The patent segments data into fixed-size blocks and represents each block as a matrix of bits, enabling granular compression at the bit level rather than operating on entire datasets or large blocks, thus reducing the overhead impact on storage efficiency
Solution Approach 2:
The patent changes the representation parameter of data from raw binary format to a matrix format where bits are organized in rows and columns, enabling systematic application of compression rules that transform data to minimum state while tracking changes through metadata
2Quantity of substance
If data blocks are optimized at original data level through existing reduction techniques, then some compression is achieved, but original datasets are not minimized sufficiently to increase storage efficiency
Solution Approach 1:
The patent introduces a new dimension of compression by organizing data bits into a matrix structure with rows and columns, enabling compression rules to operate across both dimensions and achieve minimum state representation that goes beyond traditional single-dimensional compression approaches
Solution Approach 2:
The patent introduces metadata as an intermediary that records the transformation steps from original data to compressed minimum state matrix, enabling efficient decompression while the compression rules act as intermediaries between the matrix representation and the final compressed form
Data Source
AI summary
This disclosure relates to method and system for data compression and data storage optimization. The method of compression may include converting each data block into a matrix, compressing each data block by processing the corresponding matrix to form a minimum state matrix based on a sequential set of compression rules, deriving a granular metadata for each data block based on the corresponding minimum state matrix, and storing the granular metadata and the sequential set of compression rules for each data block. Further, the method of decompression may include accessing a granular metadata and a sequential set of compression rules for each data block, deriving a minimum state matrix for each data block based on the corresponding granular metadata, decompressing each data block by processing the corresponding minimum state matrix to form a matrix based on the sequential set of compression rules, and building each data block from the corresponding matrix.