Towards an Environmental Decision-Making System: A Vocabulary to Enrich Stream Data

Peter Wetz (), Tuan-Dat Trinh, Ba-Lam Do, Amin Anjomshoaa, Elmar Kiesling and A Min Tjoa
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Peter Wetz: TU Wien
Tuan-Dat Trinh: TU Wien
Ba-Lam Do: TU Wien
Amin Anjomshoaa: TU Wien
Elmar Kiesling: TU Wien
A Min Tjoa: TU Wien

Chapter Chapter 17 in Advances and New Trends in Environmental and Energy Informatics, 2016, pp 317-335 from Springer

Abstract: Abstract The future of the earth’s environmental systems will, to a major extent, be determined in cities, where already more than 50 % of the human population is concentrated. Pervasively available sensors and the data they generate can help to address pressing environmental challenges in urban areas by making crucial information available to researchers and decision-makers. However, environmental data is at present typically stored in disparate systems and formats, which inhibits reuse and integration. Furthermore, the large amounts of environmental data that stream in continuously require novel processing approaches. So far, research at the intersection of environmental sciences and urban data infrastructures has been scarce. To address these issues, we develop a novel framework based on semantic web technologies. We apply data modeling and semantic stream processing technologies in order to facilitate integration, comparison, and visualization of heterogeneous data from various sources. This paper presents the concept of a platform for environmental data stream analysis, and focuses on the design of a new vocabulary to semantically enrich the processed streams. The implemented architecture shall be capable of informing and supporting decision-making by non-expert users. We propose and discuss a three-step framework, present a vocabulary to model environmental data streams, and outline initial results.

Keywords: Environmental data streams; Semantic sensor network ontology; RDF data cube vocabulary; Stream processing (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-23455-7_17

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