Block Storage Data Distillation for Lossless Global Deduplication
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Solution Overview
Problem
Current data compression methods are inefficient in handling large and extremely large datasets, as they can only exploit redundancy within a local window, leading to throttling by storage access bandwidth limitations and are unsuitable for random access and high-speed data ingestion and retrieval.
Innovation Solution
The Data Distillation process organizes data into Prime Data Elements stored in a Prime Data Store, allowing for content-associative search and retrieval, where data chunks are factorized into Prime and Derivative Elements, with a reconstitution program to reconstruct the original data, enabling efficient lossless reduction and retrieval across large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in traditional compression formats (e.g., PARquet, ORC) with row-oriented or column-oriented structures, then data can be accessed and processed, but the data footprint is large and storage efficiency is low
Solution Approach 1:
The patent segments data into fixed-size chunks (e.g., 128 bytes) and further divides each chunk into variable-length elements based on content boundaries. This segmentation enables selective compression of redundant elements while maintaining fast access to individual chunks, resolving the contradiction between reducing data footprint and maintaining high ingestion rates.
Solution Approach 2:
The patent creates a compact index structure that stores references to unique data elements rather than duplicating the actual data. By copying only the necessary metadata and chunk references, the system achieves significant footprint reduction while enabling rapid data retrieval through the index, thus improving both storage efficiency and ingestion performance.
2Quantity of substance
If universal lossless data reduction techniques are applied to diverse data formats, then data footprint is reduced without loss of information, but the techniques require no prior knowledge of input data structure which limits optimization
Solution Approach 1:
The patent changes the parameter of data representation by transforming raw data into a normalized element structure with explicit type markers and length fields. This parameter change enables the system to recognize and compress redundant patterns across diverse data formats while maintaining lossless reconstruction capability, achieving both footprint reduction and information preservation.
3Speed
If data is organized in storage hierarchies with fast volatile memory for frequently accessed data and dense cheaper storage for bulk data, then access speed is improved for critical data, but the complexity of managing multiple storage tiers increases
Solution Approach 1:
The patent performs preliminary organization of data into fixed-size chunks with embedded metadata during the data ingestion phase. This preliminary action creates a self-describing data structure that enables fast random access without requiring complex runtime management of storage hierarchies, thus improving access speed while reducing system complexity.
4Quantity of substance
If compression algorithms process large datasets to reduce storage requirements, then storage capacity is extended, but the processing time and computational resources increase
Solution Approach 1:
The patent segments large datasets into independent fixed-size chunks that can be processed in parallel. Each chunk is further divided into elements that are compressed independently using a compact representation scheme. This segmentation enables efficient parallel processing, reducing overall processing time while achieving significant storage capacity extension through compression.
Data Source
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AI summary
This disclosure relates to reduction of audio data and data stored on a block processing storage system. A losslessly reduced representation of an audio component can include references to one or more prime audio components, and optionally a description of a reconstitution program which, when applied to the one or more prime audio components, results in the audio component. A losslessly reduced representation of data block can include references to one or more prime data element blocks, and optionally a description of a reconstitution program which, when applied to the one or more prime data element blocks, results in the data block.