DeXOR: Enabling XOR in Decimal Space for Streaming Lossless Compression of Floating-point Data

2026-01-02

arXiv.org

AI Summary

This work proposes DeXOR, a novel compression framework addressing the inefficiency of existing methods in handling high-precision or non-smooth streaming floating-point data. DeXOR introduces XOR-based encoding directly in decimal space for the first time, leveraging longest common prefix and suffix extraction to eliminate redundancy. The framework integrates several optimization strategies—including error-tolerant rounding, scaled truncation, exponent outlier handling, and bit-level management—to enhance compression while preserving decompression accuracy. Evaluated on 22 real-world datasets, DeXOR achieves an average 15% improvement in compression ratio and a 20% increase in decompression speed, demonstrating robustness and efficiency even under extreme conditions.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Non-convex Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

With streaming floating-point numbers being increasingly prevalent, effective and efficient compression of such data is critical. Compression schemes must be able to exploit the similarity, or smoothness, of consecutive numbers and must be able to contend with extreme conditions, such as high-precision values or the absence of smoothness. We present DeXOR, a novel framework that enables decimal XOR procedure to encode decimal-space longest common prefixes and suffixes, achieving optimal prefix reuse and effective redundancy elimination. To ensure accurate and low-cost decompression even with binary-decimal conversion errors, DeXOR incorporates 1) scaled truncation with error-tolerant rounding and 2) different bit management strategies optimized for decimal XOR. Additionally, a robust exception handler enhances stability by managing floating-point exponents, maintaining high compression ratios under extreme conditions. In evaluations across 22 datasets, DeXOR surpasses state-of-the-art schemes, achieving a 15% higher compression ratio and a 20% faster decompression speed while maintaining a competitive compression speed. DeXOR also offers scalability under varying conditions and exhibits robustness in extreme scenarios where other schemes fail.

Problem

• streaming compression
• floating-point data
• lossless compression
• decimal XOR
• data redundancy

Innovation

• decimal XOR
• lossless compression
• floating-point data
• streaming compression
• error-tolerant rounding