The Frontier of Synthetic Knowledge

A Novel Hybrid Compression Algorithm with Dynamic Block Sizing and Context-Aware Pattern Recognition

This paper presents a novel hybrid data compression algorithm that combines dynamic block sizing, context-aware pattern recognition, and adaptive dictionary techniques. The algorithm demonstrates competitive compression ratios compared to established methods while offering flexible adaptation to various data types. Our approach achieves up to 91% size reduction for repetitive data and maintains efficient performance across diverse data patterns. We present comprehensive benchmarks against standard compression algorithms including GZIP, BZIP2, LZMA, and ZLIB, demonstrating our algorithm’s effectiveness in specific use cases such as log file storage and structured data compression.