Scholarlink

Manufacturers and owners use high-frequency sensor data to optimize energy production. Data is collected on the edge (i.e., wind turbines) and transferred to the cloud for analytics. General-purpose RDBMSs are unable to handle the volume and velocity of sensor data. As a remedy, Time Series Management Systems (TSMSs)Time Series Management Systems have been offered to manage sensor data across the entire pipeline efficiently. This chapter surveys TSMSs developed through academic or industrial research and documented through peer-reviewed papers. The chapter uses classification criteria for surveying systems by architecture, year, primary purpose, deployment, maturity, scale shown, data processing engine, API, approximation, latency, data store and storage layout. A collection of open research problems is provided based on the surveyed systems.

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Current Systems for Managing Massive High Frequency Time Series

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Abduvoris Abduvakhobov,
  • Søren Kejser Jensen,
  • Christian Thomsen,
  • Esteban Zimányi

摘要

Manufacturers and owners use high-frequency sensor data to optimize energy production. Data is collected on the edge (i.e., wind turbines) and transferred to the cloud for analytics. General-purpose RDBMSs are unable to handle the volume and velocity of sensor data. As a remedy, Time Series Management Systems (TSMSs)Time Series Management Systems have been offered to manage sensor data across the entire pipeline efficiently. This chapter surveys TSMSs developed through academic or industrial research and documented through peer-reviewed papers. The chapter uses classification criteria for surveying systems by architecture, year, primary purpose, deployment, maturity, scale shown, data processing engine, API, approximation, latency, data store and storage layout. A collection of open research problems is provided based on the surveyed systems.