Skip to main content
SpringerLink
Log in

A Comparison of Time Series Databases for Storing Water Quality Data

  • Conference paper
  • First Online:
Mobile Technologies and Applications for the Internet of Things (IMCL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 909))

Included in the following conference series:

Abstract

Water quality is an ongoing concern and wireless water quality sensing promises societal benefits. Our goal is to contribute to a low-cost water quality sensing system. The particular focus of this work is the selection of a database for storing water quality data. Recently, time series databases have gained popularity. This paper formulates criteria for comparison, measures selected databases and makes a recommendation for a specific database. A low-cost low-power server, such as a Raspberry Pi, can handle as many as 450 sensors’ data at the same time by using the InfluxDB time series database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. DalmatinerDB. https://dalmatiner.io/. Accessed May 30, 2018.

  2. Graphite. https://graphiteapp.org/. Accessed August 26, 2018.

  3. InfluxDB. https://www.influxdata.com/. Accessed: May 30, 2018.

  4. Kdb+. https://kx.com/. Accessed August 26, 2018.

  5. OpenTSDB. http://opentsdb.net/. Accessed May 30, 2018.

  6. Prometheus. https://prometheus.io/. Accessed August 26, 2018.

  7. RRDtool. https://oss.oetiker.ch/rrdtool/. Accessed August 26, 2018.

  8. Acreman, S. (2018). Top10 time series databases. https://blog.outlyer.com/top10-open-source-time-series-databases. Accessed August 26, 2018.

  9. Bader, A. (2016). Comparison of time series databases. Diploma thesis, Institute of Parallel and Distributed Systems, University of Stuttgart.

    Google Scholar 

  10. Deri, L., Mainardi, S., & Fusco, F. (2012). tsdb: A compressed database for time series. In A. Pescapè, L. Salgarelli, & X. Dimitropoulos (Eds.), Traffic monitoring and analysis (pp. 143–156). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  11. Gormley, C., & Tong, Z. (2015). Elasticsearch: The definitive guide: a distributed real-time search and analytics engine. O’Reilly Media, Inc.

    Google Scholar 

  12. Hsu, L., & Selvaganapathy, P. R. (2014). Stable and reusable electrochemical sensor for continuous monitoring of phosphate in water. In: SENSORS (pp. 1423–1426). 2014 IEEE. IEEE.

    Google Scholar 

  13. Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B. E., Bussonnier, M., Frederic, J., et al. (2016). Jupyter notebooks-a publishing format for reproducible computational workflows. In: F. Loizides & B. Schmidt (Eds.), Positioning and power in academic publishing: Players, agents and agendas (pp. 87–90). IOS Press.

    Google Scholar 

  14. Maier, D. (1983). The theory of relational databases (Vol. 11). Rockville: Computer Science Press.

    Google Scholar 

  15. Namiot, D. (2015). Time series databases. In Selected Papers of the XVII International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2015) (pp. 132–137), Obninsk, Russia, October 13–16, 2015, http://ceur-ws.org/Vol-1536/paper20.pdf.

  16. Nokovic, B., & Sekerinski, E. (2015). Automatically quantitative analysis and code generator for sensor systems: The example of great lakes water quality monitoring. In: International internet of things summit (pp. 313–319). Springer.

    Google Scholar 

  17. Pescapè, A., Salgarelli, L., & Dimitropoulos, X. (2012). Traffic Monitoring and Analysis: 4th International Workshop, TMA 2012 (Vol. 7189), Vienna, Austria, March 12, 2012, Proceedings. Springer Science & Business Media.

    Google Scholar 

  18. Pungilă, C., Fortiş, T. F., & Aritoni, O. (2009). Benchmarking database systems for the requirements of sensor readings. IETE Technical Review, 26(5), 342–349. Taylor & Francis. https://www.tandfonline.com/doi/abs/10.4103/0256-4602.55279.

  19. Wlodarczyk, T. W. (2012). Overview of time series storage and processing in a cloud environment. In 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings (pp. 625–628).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. McMaster University, Hamilton, ON, Canada

    Muntazir Fadhel, Emil Sekerinski & Shucai Yao

Authors
  1. Muntazir Fadhel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emil Sekerinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shucai Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emil Sekerinski .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Thrasyvoulos Tsiatsos

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fadhel, M., Sekerinski, E., Yao, S. (2019). A Comparison of Time Series Databases for Storing Water Quality Data. In: Auer, M., Tsiatsos, T. (eds) Mobile Technologies and Applications for the Internet of Things. IMCL 2018. Advances in Intelligent Systems and Computing, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-11434-3_33

Download citation

Publish with us

Policies and ethics

Search

Navigation