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Sensor Data Integration Using Ontologies for Event Detection

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Advanced Information Networking and Applications (AINA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 661))

Abstract

Nowadays, computing applications operate in environments with multiple and heterogeneous data sources, such as data generated from IoT devices. Without contextual information, the information derived from these isolated data sources may cause bias, error, or a lack of correct comprehension. Data integration can help to promote a holistic view of data and support getting the most trustful meaning from the information. This work proposes an architecture in which ontologies help to provide context for data integration. Furthermore, ontologies and complex network concepts enrich context awareness and derive relations among data to identify events of interest. The approach is evaluated in a controlled experiment using real data from hydrological and hydrometric sensors. The results indicate it is possible to detect context and relate events from different data sources to new significant events through ontology and graph network analysis.

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Notes

  1. 1.

    https://networkx.org/.

  2. 2.

    https://owlready2.readthedocs.io/en/v0.37/.

  3. 3.

    https://protege.stanford.edu/.

  4. 4.

    https://bitbucket.org/JeffersonAmara/integration-system.

  5. 5.

    https://www.gdacs.org/flooddetection/ (Accessed at 2022/10/15).

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Author information

Authors and Affiliations

  1. Graduate Program in Computer Science, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil

    Jefferson Amará, Victor Ströele & Regina Braga

  2. Department of Computer Science, University of Western Ontario (UWO), London, Canada

    Michael Bauer

Authors
  1. Jefferson Amará
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  2. Victor Ströele
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  3. Regina Braga
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  4. Michael Bauer
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Corresponding author

Correspondence to Victor Ströele .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Amará, J., Ströele, V., Braga, R., Bauer, M. (2023). Sensor Data Integration Using Ontologies for Event Detection. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-031-29056-5_17

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