From Similarities to Insights: Approaching Time Series Integration from a User Perspective

Cyber-physical systems such as buildings and power plants are increasingly monitored using large numbers of sensors, resulting in massive and heterogeneous time-series datasets. High-quality metadata - particularly measurement type and functional location - is essential to extract value from this data. However, such metadata is often incomplete or missing. While recent research addresses the issue of recovering functional location from raw time-series data, it focuses on discovering pairwise relationships and provides little guidance for end-users on how to apply these methods.