Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces
- 903 Downloads
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.
KeywordsSensor indexing Service discovery Formal concept analysis Dataspaces Intelligent systems Internet of Things
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.