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Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces

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Abstract

A dataspace is an emerging data management approach used to tackle heterogeneous data integration in an incremental manner. Data sources that are participants in a dataspace can be of various types such as online services, open datasets, sensors, and smart devices. Given the dynamicity of dataspaces and the diversity of their data sources and user requirements, finding appropriate sources of data can be challenging for users. Thus, it is important to describe and organise data sources in the dataspace efficiently. In this chapter, we present an approach for organising and indexing data services based on their semantic descriptions and using a feature-oriented model. We apply Formal Concept Analysis for organising and indexing the descriptions of sensor-based data services. We have experimented and validated the approach in a real-world smart environment which has been retrofitted with Internet of Things-based sensors observing energy, temperature, motion, and light.

Keywords

  • Sensor indexing
  • Service discovery
  • Formal concept analysis
  • Dataspaces
  • Intelligent systems
  • Internet of Things

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Correspondence to Edward Curry .

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Derguech, W., Curry, E., Bhiri, S. (2020). Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces. In: Real-time Linked Dataspaces. Springer, Cham. https://doi.org/10.1007/978-3-030-29665-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-29665-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29664-3

  • Online ISBN: 978-3-030-29665-0

  • eBook Packages: Computer ScienceComputer Science (R0)