Prime Data Sieve Locality for Lossless Large-Scale Data Retrieval
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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, are compute-intensive, and struggle with random access and high-speed data ingestion and retrieval, leading to limitations in data storage and processing capabilities.
Innovation Solution
The implementation of a Prime Data Sieve that factorizes input data into Prime Data Elements and Derivative Elements, allowing for content-associative access and transformation to achieve lossless data reduction across the entire dataset, enabling high rates of data ingestion and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data compression methods are used, then data redundancy is exploited within a local window, but the method is compute-intensive and cannot efficiently handle large datasets with global redundancy
Solution Approach 1:
The patent segments the large dataset into multiple local windows that can be processed independently. Each window is compressed using conventional methods, but the segmentation allows parallel processing and reduces the computational burden on any single processing unit. This enables handling of large datasets that would be too computationally intensive to process as a single unit.
Solution Approach 2:
The patent introduces a new dimension of processing by organizing data into a hierarchical structure with multiple levels. Instead of processing data in a single linear pass, the invention creates a multi-dimensional processing space where data can be accessed and compressed from different perspectives, enabling global redundancy exploitation across the entire dataset rather than being constrained to local windows.
2Quantity of substance
If data is stored in a storage hierarchy with multiple tiers, then storage cost is reduced, but data retrieval speed decreases due to higher latency in lower tiers
Solution Approach 1:
The patent applies preliminary compression actions to data before it is stored in the storage hierarchy. By compressing data using the novel method that exploits global redundancy patterns, the data footprint is reduced before storage, allowing more data to be stored in higher-speed tiers without proportionally increasing cost. This preliminary processing enables faster retrieval speeds while maintaining cost efficiency.
Solution Approach 2:
The patent introduces an intermediary compression layer between the data source and the storage hierarchy. This intermediary process transforms raw data into a compressed format that preserves all information while reducing size, allowing the storage system to operate more efficiently across all tiers. The compressed data acts as an intermediary representation that enables both cost-effective storage and fast retrieval.
3Adaptability or versatility
If universal lossless data reduction techniques are applied, then no prior knowledge of data structure is needed, but the techniques cannot efficiently exploit specific redundancy patterns in diverse data formats
Solution Approach 1:
The patent implements a dynamic compression system that automatically adapts to the characteristics of the input data. The system dynamically selects and applies different compression strategies based on the detected data patterns, rather than using a single static approach. This dynamic behavior enables the system to achieve high compression ratios for diverse data formats while maintaining universal applicability.
Solution Approach 2:
The patent changes key parameters of the compression process based on the input data characteristics. By dynamically adjusting compression parameters such as window size, compression level, and algorithm selection, the system optimizes the compression ratio for each specific data type while maintaining universal compatibility. This parameter adaptation resolves the contradiction between versatility and effectiveness.
Data Source
AI summary
An amount of memory needed to hold prime data elements during reconstitution may be determined by examining the creation and usage of prime data elements and their spatial and temporal characteristics during data distillation.