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Extreme-Scale Model-Based Time Series Management with ModelarDB (Invited Talk)

Author Torben Bach Pedersen



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Torben Bach Pedersen
  • Department of Computer Science, Center for Data-intensive Systems, Aalborg University, Denmark
  • ModelarData, Copenhagen, Denmark

Acknowledgements

I want to thank Søren Kejser Jensen and Christian Thomsen for their major contributions to this work.

Cite AsGet BibTex

Torben Bach Pedersen. Extreme-Scale Model-Based Time Series Management with ModelarDB (Invited Talk). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 2:1-2:2, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.TIME.2021.2

Abstract

To monitor critical industrial devices such as wind turbines, high quality sensors sampled at a high frequency are increasingly used. Current technology does not handle these extreme-scale time series well [Søren Kejser Jensen et al., 2017], so only simple aggregates are traditionally stored, removing outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing extreme-scale time series that approximates the time series values using mathematical functions (models) and stores only model coefficients rather than data values. Compression is done both for individual time series and for correlated groups of time series. The keynote will present concepts, techniques, and algorithms from model-based time series management and our implementation of these in the open source Time Series Management System (TSMS) ModelarDB[Søren Kejser Jensen et al., 2018; Søren Kejser Jensen et al., 2019; Søren Kejser Jensen et al., 2021] . Furthermore, it will present our experimental evaluation of ModelarDB on extreme-scale real-world time series, which shows that that compared to widely used Big Data formats, ModelarDB provides up to 14× faster ingestion due to high compression, 113× better compression due to its adaptability, 573× faster aggregatation by using models, and close to linear scale-out scalability. ModelarDB is being commercialized by the spin-out company ModelarData.

Subject Classification

ACM Subject Classification
  • Information systems → Data management systems
Keywords
  • Model-based storage
  • approximate query processing
  • time series management
  • extreme-scale data

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References

  1. Søren Kejser Jensen, Torben Bach Pedersen, and Christian Thomsen. Time series management systems: A survey. IEEE Trans. Knowl. Data Eng., 29(11):2581-2600, 2017. URL: https://doi.org/10.1109/TKDE.2017.2740932.
  2. Søren Kejser Jensen, Torben Bach Pedersen, and Christian Thomsen. Modelardb: Modular model-based time series management with spark and cassandra. Proc. VLDB Endow., 11(11):1688-1701, 2018. URL: https://doi.org/10.14778/3236187.3236215.
  3. Søren Kejser Jensen, Torben Bach Pedersen, and Christian Thomsen. Demonstration of modelardb: Model-based management of dimensional time series. In Peter A. Boncz, Stefan Manegold, Anastasia Ailamaki, Amol Deshpande, and Tim Kraska, editors, Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019, pages 1933-1936. ACM, 2019. URL: https://doi.org/10.1145/3299869.3320216.
  4. Søren Kejser Jensen, Torben Bach Pedersen, and Christian Thomsen. Scalable model-based management of correlated dimensional time series in modelardb+. In 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021, pages 1380-1391. IEEE, 2021. URL: https://doi.org/10.1109/ICDE51399.2021.00123.
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