A demonstration of ModelarDB was presented in the 51st International Conference on Very Large Data Bases in London, United Kingdom, September 3, 2025. Our AAU team did a great work : special thanks to Abduvoris Abduvakhobov, Søren Kejser Jensen, Christian Schmidt Godiksen, Christian Thomsen and Torben Bach Pedersen.
Demo Title: Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client.
A short background on ModelarDB:
Renewable Energy Sources (RESs) are monitored by many high quality sensors that produce vast amounts of high-frequency time series data. This can be used to increase the renewable energy production and longevity of the RESs, e.g., yaw misalignment detection and predictive maintenance for wind turbines. Currently, wind turbine manufacturers and owners cannot use this data due to limits on bandwidth and storage that are infeasible to increase. Thus, they resort to less efficient methods like storing simple aggregates which remove valuable outliers and fluctuations. As a remedy, we propose the new model-based Time Series Management System (TSMS) ModelarDB.
What happened in the demo:
The participants experienced how ModelarDB ingests time series on the edge and compresses them as segments with metadata and so-called models. The models represent values within a user-defined absolute or relative error bound (even 0 or 0%). Participants were able to adjust many parameters and see how the segments are transferred to the cloud using much less bandwidth and storage than other popular solutions like Apache Parquet e.g., up to 90%–99% less than Apache Parquet. Participants were also able to analyze the time series on the edge, in the cloud, and on the client using SQL or Python real-time. On the client, ModelarDB runs in-process to integrate with, e.g., Python. Thus, participants could see how ModelarDB efficiently manages high-frequency time series across edge, cloud, and client.
Check the LinkedIn post.