Content-Associative Sieve for Lossless Data Reduction at Scale
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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 and are compute-intensive, leading to throttling by storage access bandwidth limitations, and are not suited for random access or high-speed data ingestion and retrieval.
Innovation Solution
The Data Distillation process factorizes input data into Prime Data Elements and Derivative Elements, using a content-associative sieve to identify and reconstitute data chunks, allowing for lossless reduction and efficient retrieval by referencing Prime Data Elements and a reconstitution program, enabling data reduction across large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used to reduce data footprint, then data storage efficiency improves, but processing speed and ingestion rate deteriorate due to compute-intensive operations
Solution Approach 1:
The patent segments data into fixed-size chunks and processes them independently through the content-associative sieve. This segmentation allows parallel processing of multiple chunks simultaneously, improving throughput while maintaining compression effectiveness. Each chunk is handled as a discrete unit, enabling pipelining and reducing sequential processing bottlenecks.
Solution Approach 2:
The system performs preliminary actions by pre-processing data chunks to extract features and create representations before compression. The content-associative sieve pre-computes hashes and organizes data structures in advance, so when compression is needed, the system can quickly lookup and reference existing prime data elements without intensive real-time computation.
2Productivity
If redundancy exploitation is limited to local windows, then processing efficiency improves, but data reduction capability deteriorates due to inability to find global redundancies
Solution Approach 1:
The patent transitions from local window-based compression to a global content-associative space by hashing data chunks and organizing them in a sieve structure. This dimensional change allows the system to search across the entire dataset for redundant content rather than being constrained to local windows, enabling discovery of global redundancies while maintaining efficient lookup through hash-based indexing.
Solution Approach 2:
The content-associative sieve acts as an intermediary structure between raw data and compressed output. It hashes and indexes prime data elements, creating a mediator layer that enables efficient global redundancy detection. The sieve structure with its hash functions and bucket organization serves as the intermediary that connects input chunks to matching prime elements, allowing global search without sacrificing processing efficiency.
3Quantity of substance
If compute-intensive compression algorithms are used, then data reduction ratio improves, but storage access bandwidth utilization deteriorates due to throttling
Solution Approach 1:
The patent replaces compute-intensive mechanical compression algorithms with a lookup-based system using content-associative sieves. Instead of performing intensive computations to find redundancies, the system uses hash functions and direct table lookups to identify prime data elements. This substitution of computational mechanics with data structure-based mechanics dramatically reduces processing load and improves bandwidth utilization.
Solution Approach 2:
The system changes the parameter of redundancy detection from computational complexity to data structure organization. By organizing prime data elements in a content-associative sieve with specific hash function parameters and bucket structures, the system achieves high data reduction ratios through efficient parameterized lookup rather than intensive computation, thereby improving bandwidth efficiency.
4Quantity of substance
If data is compressed using existing methods, then storage density improves, but random access capability deteriorates due to inability to efficiently retrieve specific chunks
Solution Approach 1:
The patent creates content-associative copies and references of prime data elements in the sieve structure. When data is compressed, instead of storing only compressed bits, the system maintains reference copies in the content-associative sieve that enable rapid retrieval. These copied references act as indexes that allow random access to specific chunks without decompressing entire data streams, maintaining storage density while enabling efficient access.
Data Source
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AI summary
This disclosure relates to lossless data reduction on large and extremely large datasets while providing high rates of data ingestion and data retrieval. Some embodiments can generate a losslessly reduced representation of a data chunk, wherein the losslessly reduced representation includes a reference to one or more prime data elements stored in a content associative sieve, and optionally a description of a reconstitution program which, when applied to the one or more prime data elements results in the data chunk. The losslessly reduced representation of the data chunk can then be communicated to another computer over a network.