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Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series

Summary: Demonstrates ModelarDB, a model-based TSMS for dimensional time series with gaps, enabling fast ingestion and high compression via adaptive models under user-defined error bounds. Novel in correlation-driven grouping for improved compression and SQL-on-model querying, setting it apart from TSMSs focused on data cleaning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5786
Venue
SIGMOD
Year
2019
Pagerank
-
Overall Rank
13,496 | 7.41%
DOI
10.1145/3299869.3320216

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{jensen_sigmod19,
        title = {{Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series}},
        author = {Jensen, Søren Kejser and Pedersen, Torben Bach and Thomsen, Christian},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3320216},
        url = {https://dl.acm.org/doi/10.1145/3299869.3320216},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,339 Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB 2024 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,973 ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra 2018 VLDB 9.3673522e-05
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