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TSorage: A Modern and Resilient Platform for Time Series Management at Scale

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Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry (ESTEP 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1338))

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Abstract

This paper presents an open source platform for managing time series at scale. The platform provides innovative functional and non-functional features and services that help the user in addressing current challenges in data collection, processing and storage in an industrial context. Opportunities of using this platform for developing and managing artificial intelligence systems are discussed.

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References

  1. Brummayer, M., Chust, R., Colla, V., Gailly, E., Delsing, J., Kämper, F., Kuiper, G., Krauth, P.N., Mathis, G., Mouton, S., Peters, H., Pietrosanti, C., Sanfilippo, F., Winter, G.: Roadmap: Integrated intelligent manufacturing (i2m). ESTEP Working Group (2016)

    Google Scholar 

  2. CETIC asbl, TSorage documentation. https://doc.tsorage.io. Accessed 10 Nov 2020

  3. Acreman, S., Gies, H., Brazil, B.: Open source timeseries DB comparison. https://docs.google.com/spreadsheets/d/1sMQe9oOKhMhIVw9WmuCEWdPtAoccJ4a-IuZv4fXDHxM/pubhtml. Accessed 10 Nov 2020

  4. InfluxData, InfluxDB. https://influxdata.com. Accessed 10 Nov 2020

  5. Aveva, Aveva Historian. https://www.aveva.com/en/products/historian/. Accessed 10 Nov 2020

  6. Aspentech, InfoPlus21. https://www.aspentech.com/en/products/msc/aspen-infoplus21. Accessed 10 Nov 2020

  7. Automated Results, PI Historian. http://www.automatedresults.com/PI/pi-historian.aspx. Accessed 10 Nov 2020

  8. Thingsboard Inc, Thingsboard. https://thingsboard.io. Accessed 10 Nov 2020

  9. DataDog Inc., DataDog. https://www.datadoghq.com. Accessed 10 Nov 2020

  10. Spotify, Heroic. https://spotify.github.io/heroic/. Accessed 10 Nov 2020

  11. Van Duynhoven, A., Dragicevic, S.: Analyzing the effects of temporal resolution and classification confidence for modeling land cover change with long short-term memory networks. Remote Sens. 11, 2784 (2019)

    Article  Google Scholar 

  12. Machine Metrics, Unlocking the Power of High-Frequency Control Data. https://www.machinemetrics.com/blog/unlocking-the-power-of-high-frequency-control-data. Accessed 03 Dec 2020

  13. Huchtkoetter, J., Reinhardt, A.: On the impact of temporal data resolution on the accuracy of non-intrusive load monitoring. In: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2020, pp. 270–273, New York, NY, USA (2020). Association for Computing Machinery

    Google Scholar 

  14. Metrics 2.0 Community, Metrics 2.0. http://metrics20.org. Accessed 10 Nov 2020

  15. Apache Foundation, Apache Kafka. https://kafka.apache.org. Accessed 10 Nov 2020

  16. Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., Lennartson, B.: An event-driven manufacturing information system architecture for industry 4.0. Int. J. Product. Res. 55(5), 1297–1311 (2017)

    Google Scholar 

  17. Apache Foundation, Apache Cassandra. https://cassandra.apache.org. Accessed 10 Nov 2020

  18. Prometheus Authors, Querying Prometheus. https://prometheus.io/docs/prometheus/latest/querying/basics/. Accessed 10 Nov 2020

  19. Grafana Labs, Grafana. https://grafana.com. Accessed 10 Nov 2020

  20. CETIC asbl, ARTEMTEC. https://www.cetic.be/ARTEMTEC-en. Accessed 10 Nov 2020

  21. Zhao, Y., Nasrullah, Z., Li, Z.: PyOD: a python toolbox for scalable outlier detection. J. Mach. Learn. Res. 20(96), 1–7 (2019)

    MathSciNet  Google Scholar 

  22. ElasticSearch B.V., ElasticSearch. https://www.elastic.co. Accessed 10 Nov 2020

  23. Rauch, L., Pietrzyk, M.: Digital twins as a modern approach to design of industrial processes. J. Mach. Eng. 19, 86–97 (2019)

    Google Scholar 

  24. Tao, F., Zhang, H., Liu, A., Nee, A.Y.C.: Digital twin in industry: state-of-the-art. IEEE Trans. Ind. Inform. 15(4), 2405–2415 (2019)

    Article  Google Scholar 

  25. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4(1), 237–285 (1996)

    Article  Google Scholar 

  26. CETIC asbl, FADI. https://github.com/cetic/fadi. Accessed 10 Nov 2020

  27. CETIC asbl, TSimulus. https://github.com/cetic/TSimulus. Accessed 10 Nov 2020

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Acknowledgement

This work was carried out as part of the ARTEMTEC project, supported by Wallonia (Walloon Region) under grant agreement N°7904.

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Authors and Affiliations

  1. Centre d’Excellence en Technologies de l’Information et de la Communication, Gosselies, Belgium

    Mathieu Goeminne, Nico Salamone, Mohamed Boukhebouze & Stéphane Mouton

Authors
  1. Mathieu Goeminne
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  2. Nico Salamone
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  3. Mohamed Boukhebouze
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  4. Stéphane Mouton
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Corresponding author

Correspondence to Mathieu Goeminne .

Editor information

Editors and Affiliations

  1. TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Valentina Colla

  2. Danieli Automation, Buttrio, Italy

    Costanzo Pietrosanti

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Cite this paper

Goeminne, M., Salamone, N., Boukhebouze, M., Mouton, S. (2021). TSorage: A Modern and Resilient Platform for Time Series Management at Scale. In: Colla, V., Pietrosanti, C. (eds) Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry. ESTEP 2020. Advances in Intelligent Systems and Computing, vol 1338. Springer, Cham. https://doi.org/10.1007/978-3-030-69367-1_12

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