Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client
Summary: ModelarDB ingests high-frequency time series at the edge, compressing them into modelled segments with metadata and user-defined absolute/relative error bounds (including 0%). Segments stream to cloud and run in-process on clients for SQL/Python analytics, reducing bandwidth/storage by up to 90–99% vs Parquet/TsFile while preserving outliers. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 210 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.0003404384 |
| 2,140 | Online Piece-wise Linear Approximation of Numerical Streams with Precision Guarantees* | 2009 | VLDB | 9.4626098e-05 |
| 11,133 | Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB | 2024 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next