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IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for...

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Autores principales: Elsaleh, Tarek, Enshaeifar, Shirin, Rezvani, Roonak, Acton, Sahr Thomas, Janeiko, Valentinas, Bermudez-Edo, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071512/
https://www.ncbi.nlm.nih.gov/pubmed/32053898
http://dx.doi.org/10.3390/s20040953
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author Elsaleh, Tarek
Enshaeifar, Shirin
Rezvani, Roonak
Acton, Sahr Thomas
Janeiko, Valentinas
Bermudez-Edo, Maria
author_facet Elsaleh, Tarek
Enshaeifar, Shirin
Rezvani, Roonak
Acton, Sahr Thomas
Janeiko, Valentinas
Bermudez-Edo, Maria
author_sort Elsaleh, Tarek
collection PubMed
description With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.
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spelling pubmed-70715122020-03-19 IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services † Elsaleh, Tarek Enshaeifar, Shirin Rezvani, Roonak Acton, Sahr Thomas Janeiko, Valentinas Bermudez-Edo, Maria Sensors (Basel) Article With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT. MDPI 2020-02-11 /pmc/articles/PMC7071512/ /pubmed/32053898 http://dx.doi.org/10.3390/s20040953 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Elsaleh, Tarek
Enshaeifar, Shirin
Rezvani, Roonak
Acton, Sahr Thomas
Janeiko, Valentinas
Bermudez-Edo, Maria
IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title_full IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title_fullStr IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title_full_unstemmed IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title_short IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services †
title_sort iot-stream: a lightweight ontology for internet of things data streams and its use with data analytics and event detection services †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071512/
https://www.ncbi.nlm.nih.gov/pubmed/32053898
http://dx.doi.org/10.3390/s20040953
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