Toward Semantic Sensor Data Archives on the Web
Year: 2016
Type: article
Abstract: Sensor datasets on the Web are becoming increasingly available, and there is a need for making them discoverable and accessible, so that they can be reused despite their heterogeneity. While RDF and Linked Data provide fundamental principles for sharing data on the Web, it is evidenced that they have limitations for efficiently transmitting and archiving sensor data. In this paper we identify some of the main challenges for engineering semantic sensor data archives, and we present an abstract architecture for such type of infrastructure. The proposed approach is based on a mix of RDF metadata and raw sensor archives with RDF mappings, so that data can be RDF-ized on demand. We use a real sensor deployment for air quality monitoring as a motivating use case and running example, and we show preliminary results on RDF transformation, compared with a representative data compression algorithm.
Authors: Jean-Paul Calbimonte, Karl Aberer
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2469981665