Aggregating Linked Sensor Data

Christoph Stasch, Sven Schade, Alejandro Llaves, K. Janowicz, Arne Bröring

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
7 Downloads (Pure)

Abstract

Sensor observations are usually oered in relation to a specific purpose, e.g., for reporting fine dust emissions, following strict procedures, and spatio-temporal scales. Consequently, the huge amount of data gathered by today's public and private sensor networks is most often not reused outside of its initial creation context. Fostering the reusability of observations and derived applications calls for (i) spatial, temporal, and thematic aggregation of measured values, and (ii) easy integration mechanisms with external data sources. In this paper, we investigate how work on sensor observation aggregation can be incorporated into a Linked Data framework focusing on external linkage as well as provenance information. We show that Linked Data adds new aspects to the aggregation problem, e.g., whether external links from one of the original observations can be preserved for the aggregate. The Stimulus-Sensor- Observation (SSO) ontology design pattern is extended by classes and relations necessary to model the aggregation of sensor observations.
Original languageEnglish
Title of host publicationSemantic Sensor Networks 2011
Subtitle of host publicationProceedings of the 4th International Workshop on Semantic Sensor Networks
EditorsKerry Taylor, Arun Ayyagari, David De Roure
PublisherCEUR
Pages55-68
Publication statusPublished - 23 Oct 2011
Event4th International Workshop on Semantic Sensor Networks, SSN 2011 - Bonn, Germany
Duration: 23 Oct 201123 Oct 2011
Conference number: 4

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR.org
Volume839
ISSN (Print)1613-0073

Conference

Conference4th International Workshop on Semantic Sensor Networks, SSN 2011
Abbreviated titleSSN
CountryGermany
CityBonn
Period23/10/1123/10/11

Keywords

  • METIS-306576
  • Sensor Aggregation
  • Semantic Enablement
  • Linked Data

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