Ruben Verborgh’s data

Matches in Ruben’s data for { <https://dx.doi.org/10.5220/0006733106710679> ?p ?o }

• 0006733106710679 author pieter_bonte.
• 0006733106710679 author filip_de_turck.
• 0006733106710679 author femke_ongenae.
• 0006733106710679 author dorthe_arndt.
• 0006733106710679 author alexander_dejonghe.
• 0006733106710679 author me.
• 0006733106710679 creator pieter_bonte.
• 0006733106710679 creator filip_de_turck.
• 0006733106710679 creator femke_ongenae.
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• 0006733106710679 about IoT.
• 0006733106710679 about proof.
• 0006733106710679 about Notation_.
• 0006733106710679 about reasoning.
• 0006733106710679 about Semantic_Web.
• 0006733106710679 about World_Wide_Web.
• 0006733106710679 author pieter_bonte.
• 0006733106710679 author filip_de_turck.
• 0006733106710679 author femke_ongenae.
• 0006733106710679 author dorthe_arndt.
• 0006733106710679 author alexander_dejonghe.
• 0006733106710679 author me.
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• 0006733106710679 type ScholarlyArticle.
• 0006733106710679 type Article.
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• 0006733106710679 maker alexander_dejonghe.
• 0006733106710679 maker me.
• 0006733106710679 title "SENSdesc: Connect Sensor queries and Context".
• 0006733106710679 isPartOf proceedings_of_the_11th_international_joint_conference_on_biomedical_engineering_systems_and_technologies.
• 0006733106710679 name "SENSdesc: Connect Sensor queries and Context".
• 0006733106710679 label "SENSdesc: Connect Sensor queries and Context".
• 0006733106710679 name "SENSdesc: Connect Sensor queries and Context".
• 0006733106710679 topic IoT.
• 0006733106710679 topic proof.
• 0006733106710679 topic Notation_.
• 0006733106710679 topic reasoning.
• 0006733106710679 topic Semantic_Web.
• 0006733106710679 topic World_Wide_Web.
• 0006733106710679 subject IoT.
• 0006733106710679 subject proof.
• 0006733106710679 subject Notation_.
• 0006733106710679 subject reasoning.
• 0006733106710679 subject Semantic_Web.
• 0006733106710679 subject World_Wide_Web.
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• 0006733106710679 topic IoT.
• 0006733106710679 topic proof.
• 0006733106710679 topic Notation_.
• 0006733106710679 topic reasoning.
• 0006733106710679 topic Semantic_Web.
• 0006733106710679 topic World_Wide_Web.
• 0006733106710679 abstract "Modern developments confront us with an ever increasing amount of streaming data: different sensors in environments like hospitals or factories communicate their measurements to other applications. Having this data at disposal faces us with a new challenge: the data needs to be integrated to existing frameworks. As the availability of sensors can rapidly change, these need to be flexible enough to easily incorporate new systems without having to be explicitly configured. Semantic Web applications offer a solution for that enabling computers to “understand” data. But for them the pure amount of data and different possible queries which can be performed on it can form an obstacle. This paper tackles this problem: we present a formalism to describe stream queries in the ontology context in which they might become relevant. These descriptions enable us to automatically decide based on the actual setting and the problem to be solved which and how sensors should be monitored further. This helps us to limit the streaming data taken into account for reasoning tasks and make stream reasoning more performant. We illustrate our approach on a health-care use case where different sensors are used to measure data on patients and their surrounding in a hospital.".
• 0006733106710679 datePublished "2018".
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