A Review of Key Technologies for Time-Series Data Storage in Embedded Environments

This article delves into the key technologies for time-series data storage in embedded environments. Time-series data, arranged in chronological order, is crucial for various applications such as IoT, finance, and industrial monitoring. Time Series Databases (TSDBs) excel in managing large-scale, high-frequency time-series data, leveraging column-store storage and partitioning techniques for efficient data compression and accelerated query speeds. However, deploying TSDBs in embedded systems poses challenges due to resource constraints, real-time requirements, and low power consumption needs. The article analyzes existing TSDB data models and data structures, focusing on cost-effective historical data storage and handling out-of-order data to reduce storage space and conserve computational resources. It highlights the challenges for adapting TSDBs to embedded environments.