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. From the user's perspective, we identify three open challenges in the current research on functional location inference: selecting the appropriate relationship discovery algorithm, minimizing computational effort, and interpreting the results to assign locations. We examine each challenge in detail and explore potential solutions. As a first step towards interpretability, we demonstrate how to visualize pairwise similarities using matrix and scatter plots to keep the user in the loop. Using seven datasets and five pairwise relationship measures, we find that simulated annealing is effective for matrix reordering, while t-SNE and UMAP provide the best two-dimensional embeddings for preserving local structure.