Prime Data Sieve for Lossless Reduction Across Large Datasets
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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 not suited for random access or 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 organizes Prime Data Elements based on their contents, allowing for content-associative lookups and transformations to reduce data elements, enabling efficient lossless reduction of data across large datasets while maintaining high ingestion and retrieval rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data compression methods are used to reduce data footprint, then storage cost is reduced, but data retrieval speed and processing efficiency deteriorate due to the need to decompress data and limitations in exploiting redundancy beyond local windows
Solution Approach 1:
The patent segments data into fixed-size chunks and organizes them in a hierarchical tree structure with metadata layers. This segmentation enables selective access to specific data portions without decompressing entire datasets, thereby maintaining fast retrieval speed while achieving global redundancy exploitation across the segmented data structure.
Solution Approach 2:
The patent performs preliminary organization of data into a hierarchical structure with pre-computed metadata layers during the data ingestion phase. This preliminary action enables subsequent fast retrieval operations to directly access and filter data through metadata without requiring full decompression, thus improving retrieval speed while maintaining reduced data footprint.
2Volume of stationary object
If data is stored in compressed format to reduce storage requirements, then storage density is improved, but random access capability and processing efficiency worsen due to decompression requirements
Solution Approach 1:
The patent divides compressed data into fixed-size chunks organized in a hierarchical tree structure with multiple metadata layers. This segmentation enables random access to specific chunks through metadata filtering without requiring decompression of the entire dataset, thus maintaining high storage density while improving random access capability.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data structure with multiple metadata layers (L0, L1, L2, etc.) that organize data chunks at different levels of granularity. This dimensional organization enables efficient filtering and access to specific data portions without full decompression, maintaining storage density while enhancing random access capability.
3Quantity of substance
If existing compression techniques are applied to large datasets, then data reduction is achieved within local windows, but the ability to exploit global redundancy across entire datasets deteriorates due to window size limitations
Solution Approach 1:
The patent creates a universal hierarchical data structure that can exploit redundancy at multiple scales simultaneously - from local chunks to global datasets. The multi-layer metadata structure enables the system to adaptively exploit redundancy whether it exists locally within chunks or globally across the entire dataset, thus improving both data reduction ratio and global redundancy exploitation capability.
Data Source
AI summary
Input data can be losslessly reduced by using a data structure that organizes prime data elements based on their contents. Alternatively, the data structure can organize prime data elements based on the contents of a name that is derived from the prime data elements. Specifically, video data can be losslessly reduced by (1) using the data structure to identify a set of prime data elements, and (2) using the set of prime data elements to losslessly reduce intra-frames. The input data can be dynamically partitioned based on the memory usage of components of the data structure. Parcels can be created based on the partitions to facilitate archiving and movement of the data. The losslessly reduced data can be stored using a set of distilled files and a set of prime data element files.