Research on the Optimization of Storage Strategies for EMU On-Board Data

This paper conducts an optimization study on the storage strategy of EMU on-board data, proposing a performance ratio-based weighted calculation method to address storage challenges in a big data environment. EMU on-board data is crucial for ensuring the safety and efficiency of train operations, yet the surge in data volume presents significant storage and processing difficulties. By evaluating two prominent big data storage solutions, Hbase and Hive, and analyzing the performance impact of snappy and gzip compression methods, the experiment reveals that Hive-snappy demonstrates superior query performance, while Hbase-gzip shows a clear advantage in space efficiency. The paper introduces time efficiency and space efficiency indicators and combines them with a comprehensive weighted performance ratio (WPR) to provide a scientific basis for data storage strategy in the railway industry. Future research will delve into data compression technology, storage strategy optimization, real-time data processing, data security and durability, and cross-domain applications to promote technological advancement in related industries.