Floating-Point Binary Embeddings for Low-Distortion Compaction
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Solution Overview
Problem
Current data storage technologies face limitations in capacity and efficiency, particularly with the exponential growth of data demand exceeding storage capacity, and existing encoding methods do not allow for maximum compaction or security, especially with the rise of multimedia data and quantum computing.
Innovation Solution
A system and method for low-distortion compaction of floating-point numbers using a pre-encoder, data deconstruction engine, library manager, and data reconstruction engine, which pre-encodes floating-point numbers into binary string representations and indexes them for efficient compaction and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency improves, but data loss occurs (either through lossy compression or limited effectiveness with multimedia data)
Solution Approach 1:
The patent changes the parameter representation of floating-point numbers by pre-encoding them into binary string representations that are optimized for compression. This transformation alters the data format to enable better compression ratios while maintaining exact representation, thus improving storage capacity without data loss.
Solution Approach 2:
The patent applies preliminary encoding of floating-point numbers into binary string representations before the main compression process. This pre-encoding step prepares the data in a format that maximizes subsequent compression effectiveness, enabling higher compression ratios while preserving all original information.
2Device complexity
If a single encoding algorithm is used for all data, then system complexity is reduced, but maximum encoding compaction cannot be achieved
Solution Approach 1:
The patent segments the encoding process into distinct stages: pre-encoding of floating-point numbers into binary string representations, followed by main compression processing. This segmentation allows each stage to be optimized independently, achieving maximum compaction efficiency while keeping the overall system manageable through modular design.
Solution Approach 2:
The patent introduces a preliminary encoding stage that transforms floating-point numbers into binary string representations before the main compression algorithm is applied. This preliminary action prepares the data in an optimal format for compression, enabling higher compaction efficiency without requiring a completely complex new system.
3Quantity of substance
If physical storage capacity is increased to meet data demand, then storage capacity improves, but the solution is not sustainable as demand outstrips manufacturing capacity
Solution Approach 1:
The patent changes the fundamental parameter representation of data by pre-encoding floating-point numbers into binary string representations. This parameter transformation enables significantly higher compression ratios, effectively increasing storage capacity through more efficient data encoding rather than through physical expansion, providing a sustainable solution that can keep pace with growing data demands.
Data Source
AI summary
A system and method for low-distortion compaction of floating-point numbers comprising a pre-encoder, a data deconstruction engine, a library manager, a codeword storage, and a data reconstruction engine. A pre-encoder may receive a plurality of data sourcepackets with may contain one or more floating-point numbers and the received data sourcepackets are scanned to identify floating-point numbers and the identified floating-point numbers. Identified floating-point numbers may be pre-encoded into binary string representations which are low-distortion embeddings of real numbers into a Hamming space. The binary string representation may be indexed to indicate it represents a floating-point number before being compacted by a data deconstruction engine and library manager. The pre-encoding of floating-point numbers located within a sourcepacket enables the system to maximize the benefit of the compaction capabilities of the data deconstruction engine.