Skip to content

Research at St Andrews

A reference architecture and model for sensor data warehousing

Research output: Contribution to journalArticle

Author(s)

Simon Andrew Dobson, Matteo Golfarelli, Simone Graziani, Stefano Rizzi

School/Research organisations

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.
Close

Details

Original languageEnglish
Pages (from-to)7659-7670
Number of pages12
JournalIEEE Sensors Journal
Volume18
Issue number18
Early online date31 Jul 2018
DOIs
Publication statusPublished - 15 Sep 2018

    Research areas

  • Data warehouse, Multidimensional modelling, Sensor networks, Data analytics

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models

    Fang, L., Ye, J. & Dobson, S. A., 16 Jun 2019, Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019). IEEE Computer Society, p. 33-42 8780572

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Self-organization and resilience for networked systems: design principles and open research issues

    Dobson, S. A., Hutchison, D., Mauthe, A., Schaefer-Filho, A., Smith, P. & Sterbenz, J., Apr 2019, In : Proceedings of the IEEE. 107, 4, p. 819 - 834

    Research output: Contribution to journalArticle

  3. Making sense of the world: framing models for trustworthy sensor-driven systems

    Calder, M., Dobson, S., Fisher, M. & McCann, J., 15 Nov 2018, In : Computers. 7, 4, 13 p., 62.

    Research output: Contribution to journalArticle

Related by journal

  1. Developing next-generation brain sensing technologies: a review

    Robinson, J. T., Pohlmeyer, E., Gather, M. C., Kemere, C., Kitching, J. E., Malliaras, G. G., Marbleston, A., Shepard, K. L., Stieglitz, T. & Xie, C., 25 Jul 2019, In : IEEE Sensors Journal. Early Access

    Research output: Contribution to journalReview article

  2. Simple electrical modulation scheme for laser feedback imaging

    Bertling, K., Taimre, T., Agnew, G., Lim, Y., Dean, P., Indjin, D., Höfling, S., Weih, R., Kamp, M., von Edlinger, M., Koeth, J. & Rakic, A., 1 Apr 2016, In : IEEE Sensors Journal. 16, 7

    Research output: Contribution to journalArticle

ID: 255030293