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A reference architecture and model for sensor data warehousing

Research output: Contribution to journalArticlepeer-review

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A reference architecture and model for sensor data warehousing. / Dobson, Simon Andrew; Golfarelli, Matteo; Graziani, Simone; Rizzi, Stefano.

In: IEEE Sensors Journal, Vol. 18, No. 18, 15.09.2018, p. 7659-7670.

Research output: Contribution to journalArticlepeer-review

Harvard

Dobson, SA, Golfarelli, M, Graziani, S & Rizzi, S 2018, 'A reference architecture and model for sensor data warehousing', IEEE Sensors Journal, vol. 18, no. 18, pp. 7659-7670. https://doi.org/10.1109/JSEN.2018.2861327

APA

Dobson, S. A., Golfarelli, M., Graziani, S., & Rizzi, S. (2018). A reference architecture and model for sensor data warehousing. IEEE Sensors Journal, 18(18), 7659-7670. https://doi.org/10.1109/JSEN.2018.2861327

Vancouver

Dobson SA, Golfarelli M, Graziani S, Rizzi S. A reference architecture and model for sensor data warehousing. IEEE Sensors Journal. 2018 Sep 15;18(18):7659-7670. https://doi.org/10.1109/JSEN.2018.2861327

Author

Dobson, Simon Andrew ; Golfarelli, Matteo ; Graziani, Simone ; Rizzi, Stefano. / A reference architecture and model for sensor data warehousing. In: IEEE Sensors Journal. 2018 ; Vol. 18, No. 18. pp. 7659-7670.

Bibtex - Download

@article{fc96e9d762674b988072a375ad3e7eba,
title = "A reference architecture and model for sensor data warehousing",
abstract = "Sensor data is becoming far more available thanks to the growth in both sensor systems and Internet of Things devices. Much of the value of sensor data comes from examining trends that occur over long timescales, ranging from hours to years. However, making use of data a long time after it has been collected has significant implications for the data-handling systems used to manage it. In particular, the data must be contextualised into the environment in which it was collected to avoid misleading (and potentially dangerous) mis-interpretation. We apply data warehousing techniques to develop an extensible model to capture contextual metadata alongside sensor datasets, and show how this can be used to support the analysis of datasets long after collection. We present our baseline reference framework for sensor context and derive multidimensional schemata representing different modelling and analysis scenarios. Finally, we exercise the model with two case studies.",
keywords = "Data warehouse, Multidimensional modelling, Sensor networks, Data analytics",
author = "Dobson, {Simon Andrew} and Matteo Golfarelli and Simone Graziani and Stefano Rizzi",
note = "Funding: UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”.",
year = "2018",
month = sep,
day = "15",
doi = "10.1109/JSEN.2018.2861327",
language = "English",
volume = "18",
pages = "7659--7670",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "18",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A reference architecture and model for sensor data warehousing

AU - Dobson, Simon Andrew

AU - Golfarelli, Matteo

AU - Graziani, Simone

AU - Rizzi, Stefano

N1 - Funding: UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”.

PY - 2018/9/15

Y1 - 2018/9/15

N2 - Sensor data is becoming far more available thanks to the growth in both sensor systems and Internet of Things devices. Much of the value of sensor data comes from examining trends that occur over long timescales, ranging from hours to years. However, making use of data a long time after it has been collected has significant implications for the data-handling systems used to manage it. In particular, the data must be contextualised into the environment in which it was collected to avoid misleading (and potentially dangerous) mis-interpretation. We apply data warehousing techniques to develop an extensible model to capture contextual metadata alongside sensor datasets, and show how this can be used to support the analysis of datasets long after collection. We present our baseline reference framework for sensor context and derive multidimensional schemata representing different modelling and analysis scenarios. Finally, we exercise the model with two case studies.

AB - Sensor data is becoming far more available thanks to the growth in both sensor systems and Internet of Things devices. Much of the value of sensor data comes from examining trends that occur over long timescales, ranging from hours to years. However, making use of data a long time after it has been collected has significant implications for the data-handling systems used to manage it. In particular, the data must be contextualised into the environment in which it was collected to avoid misleading (and potentially dangerous) mis-interpretation. We apply data warehousing techniques to develop an extensible model to capture contextual metadata alongside sensor datasets, and show how this can be used to support the analysis of datasets long after collection. We present our baseline reference framework for sensor context and derive multidimensional schemata representing different modelling and analysis scenarios. Finally, we exercise the model with two case studies.

KW - Data warehouse

KW - Multidimensional modelling

KW - Sensor networks

KW - Data analytics

U2 - 10.1109/JSEN.2018.2861327

DO - 10.1109/JSEN.2018.2861327

M3 - Article

VL - 18

SP - 7659

EP - 7670

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 18

ER -

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