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Performance Evaluation of Embedded Time Series Indexes Using Bitmaps, Partitioning, and Trees

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Sensor Networks (SENSORNETS 2021, SENSORNETS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1674))

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Abstract

Sensor devices collecting, storing, and processing data use index structures to improve query performance. Indexing approaches based on trees, hash and range partitioning, and space efficient summaries such as bitmap indexes have been proposed. The most efficient technique to use for a particular use case is often unknown, and there has been limited research comparing the different approaches to determine situations where each is the most effective. This work presents an experimental evaluation of the sequential binary index for time series (SBITS) versus tree and partition index structures. SBITS uses space-efficient bitmap indexes and sequential writes that results in significantly higher insert and query performance. It also has the ability to adapt the index structure to both the query requirements and data distribution.

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Notes

  1. 1.

    https://www.microchip.com/en-us/product/ATmega2560.

  2. 2.

    https://www.dialog-semiconductor.com/products/memory/dataflash-spi-memory.

  3. 3.

    https://www.dialog-semiconductor.com/products/memory/dataflash-spi-memory.

  4. 4.

    https://www-k12.atmos.washington.edu/k12/grayskies/.

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Acknowledgment

The authors would like to thank NSERC for supporting this research.

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Authors and Affiliations

  1. University of British Columbia, 3187 University Way, Kelowna, BC, V1V 1V7, Canada

    Nadir Ould-Khessal, Scott Fazackerley & Ramon Lawrence

Authors
  1. Nadir Ould-Khessal
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  2. Scott Fazackerley
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  3. Ramon Lawrence
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Corresponding author

Correspondence to Ramon Lawrence .

Editor information

Editors and Affiliations

  1. Hochschule Wismar, University of Technology Business and Design, Wismar, Germany

    Andreas Ahrens

  2. Delft University of Technology, Delft, The Netherlands

    RangaRao Venkatesha Prasad

  3. Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Madrid, Spain

    César Benavente-Peces

  4. New Jersey Institute of Technology, Newark, NJ, USA

    Nirwan Ansari

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Ould-Khessal, N., Fazackerley, S., Lawrence, R. (2022). Performance Evaluation of Embedded Time Series Indexes Using Bitmaps, Partitioning, and Trees. In: Ahrens, A., Prasad, R.V., Benavente-Peces, C., Ansari, N. (eds) Sensor Networks. SENSORNETS SENSORNETS 2021 2020. Communications in Computer and Information Science, vol 1674. Springer, Cham. https://doi.org/10.1007/978-3-031-17718-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-17718-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17717-0

  • Online ISBN: 978-3-031-17718-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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