A Lossless Fuzzy Binary AND/OR Compressor

TL;DR

This paper introduces a lossless fuzzy binary AND/OR compression algorithm that enhances data compression ratios up to approximately 3:1, utilizing a four-layer technique involving fuzzy quantum conversions and encryption for efficient data storage and retrieval.

Contribution

The paper presents a novel fuzzy binary AND/OR compression model with a four-layer architecture that achieves higher lossless compression ratios than existing methods.

Findings

Achieves lossless compression ratios of 2:1 up to approximately 3:1.

Utilizes fuzzy quantum indicators for data pattern matching during decompression.

Hypothesizes potential for ratios up to 64:1 using fuzzy qubit indexing.

Abstract

In this report, a new fuzzy 2bit-AND parallel-to-OR, or simply, a fuzzy binary AND/OR (FBAR) text data compression model as an algorithm is suggested for bettering spatial locality limits on nodes during database transactions. The current model incorporates a four-layer application technique: string-to-AND/OR pairwise binary bit + fuzzy quantum with noise conversions. This technique promotes a lossless data compression ratio of 2:1 up to values approximately = 3:1, generating a spatially-efficient compressed data file compared to nowadays data compressors. Data decompression/specific data reconstruction initiates an AND/OR pattern match technique in respect of fuzzy quantum indicators in the binary function field. The reconstruction of data occurs in the 4th layer using encryption methods. It is hypothesized that significant data compression ratio of 2n:1 for n>3:1 ratios, e.g., 32~64:1 are achievable via fuzzy qubit indexing over classical byte blocks for every bit position fragmented into a (1/2 upper +1/2 lower)-bit noise frequency parallel to its counterpart signal comprised of AND/ORed-bit polarity orientation, ready for an identical data decompression.