Abstract
Efficient blockchain querying is crucial for unlocking the full potential of blockchain technology in diverse applications and enabling it to compete with traditional databases in data management. This paper comprehensively analyzes existing literature and presents a taxonomy of state-of-the-art techniques employed to optimize query efficiency in blockchain systems. We highlight the strengths of these techniques, such as enhanced scalability and real-time data access, while acknowledging their limitations, including high resource demands and limited applicability in heterogeneous blockchain environments. Additionally, our review identifies key gaps, such as the lack of a universal query language and standardized evaluation benchmarks, and discusses challenges related to balancing efficiency with privacy and security. Furthermore, we explore the potential of learned indexes and Artificial Intelligence techniques to revolutionize blockchain querying, offering a roadmap for future research aimed at addressing these challenges and gaps
