A Language-Agnostic Compression Framework for the Bitcoin Blockchain

This research addresses the growing interdisciplinary interest in Bitcoin by proposing a versatile compression framework for transforming raw blockchain data into a streamlined compact format suitable for high-performance analysis. Our approach focuses on developing a language-agnostic API, ensuring accessibility across programming languages. Beyond data extraction, our framework outputs the Bitcoin user transaction graph, facilitating network analysis, forensics, and pattern detection. Processed data are exported to the HDF5 file format for compatibility with mainstream analysis tools. A proof-of-concept CPython implementation demonstrates the framework’s feasibility, showcasing its real-world applicability for data-driven investigations in Bitcoin research.