Floating-Point Pre-Encoding for Loss-Aware Data Compaction
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity to store it, leads to a bottleneck in data storage technology, with existing solutions like data compression and physical storage expansion being insufficient.
Innovation Solution
A system and method for compaction of floating-point numbers within a dataset, utilizing a pre-encoder, data deconstruction engine, library manager, codeword storage, and data reconstruction engine to pre-encode floating-point numbers into low-distortion binary string representations, maximizing compaction benefits.
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 degradation occurs with lossy compression and space savings are insufficient with lossless compression
Solution Approach 1:
The patent transforms floating-point numbers into a different representation format (fixed-point integers) by changing the numerical parameter system. This allows the data to be stored in a more compact integer format while preserving all original information, achieving both high compression ratios and lossless reconstruction. The transformation changes the state of the data from floating-point to integer representation, enabling efficient storage without information loss.
2Ease of operation
If a single encoding algorithm is used for all data, then decoding simplicity is maintained, but maximum encoding compaction cannot be achieved and security vulnerabilities arise
Solution Approach 1:
The patent segments the encoding process into distinct stages: identification of floating-point numbers, transformation to fixed-point integers, and storage. This segmentation allows different encoding strategies to be applied to different data types within the same dataset, maximizing compaction for floating-point numbers while maintaining simple decoding through the use of type indicators that guide the reconstruction process.
Solution Approach 2:
The patent performs preliminary identification and transformation of floating-point numbers before storage. By pre-converting floating-point numbers to their fixed-point integer representations and marking them with type indicators, the system prepares the data in advance for efficient storage and simple later reconstruction, avoiding the need for complex real-time decoding decisions.
3Quantity of substance
If physical storage capacity is expanded to meet demand, then storage availability increases, but the fundamental problem of insufficient manufacturing capacity versus exponential data growth remains
Solution Approach 1:
The patent applies parameter changes by transforming floating-point data into a more compact integer representation, effectively increasing the information density of the storage medium. This doesn't require additional physical storage capacity but rather maximizes the utilization of existing capacity by storing more information in the same physical space through efficient encoding.
Data Source
AI summary
A system and method for compaction of floating-point numbers within a dataset, 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.