Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB

Modern wind turbines are monitored by sensors that generate massive amounts of high frequency time series that are ingested on the edge and then transferred to the cloud where they are stored and analyzed. This results in at least four challenges: (1) Limited hardware makes efficient ingestion necessary to keep up; (2) Limited bandwidth makes data compression necessary; (3) High storage costs as all data must be stored; and (4) Low data quality due to lossy compression methods without error bounds. Practitioners currently use solutions that only solve some of these. In this paper, we evaluate the Time Series Management System ModelarDB, a solution that meets all four challenges by efficiently managing time series across the entire pipeline. We compare it to three commonly used alternatives and evaluate different aspects of them in a realistic edge-to-cloud scenario with real-world datasets. For lossless compression, ModelarDB achieves up to 2x better compression and 1.2x better transfer efficiency. For lossy compression, ModelarDB achieves up to 4.6x better compression and 10x better transfer efficiency, or similar compression with orders of magnitude less error.