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Sift’s core data model organizes time-series data into Assets, Channels, and Runs three building blocks that structure sensor data for efficient management, analysis, and reuse across different experiments or observation periods.
  • Assets: These represent physical or virtual systems that generate data.
  • Channels: These represent individual time-series signals recorded from an Asset.
  • Runs: These represent time windows or observation sessions during which data is collected from one or more Channels.