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2017 | OriginalPaper | Buchkapitel

EO Big Data Connectors and Analytics for Understanding the Effects of Climate Change on Migratory Trends of Marine Wildlife

verfasst von : Z. A. Sabeur, G. Correndo, G. Veres, B. Arbab-Zavar, J. Lorenzo, T. Habib, A. Haugommard, F. Martin, J.-M. Zigna, G. Weller

Erschienen in: Environmental Software Systems. Computer Science for Environmental Protection

Verlag: Springer International Publishing

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Abstract

This paper describes the current ongoing research activities concerning the intelligent management and processing of Earth Observation (EO) big data together with the implementation of data connectors, advanced data analytics and Knowledge Base services to a Big Data platform in the EO4Wildlife project (www.​eo4wildlife.​eu). These components support on the discovery of marine wildlife migratory behaviours, some of which may be a direct consequence of the changing Met-Ocean resources and the globe climatic changes. In EO4wildlife, we specifically focus on the implementation of web-enabled advanced analytics web services which comply with OGC standards and make them accessible to a wide research community for investigating on trends of animal behaviour around specific marine regions of interest. Big data connectors and a catalogue service are being installed to enable access to COPERNICUS sentinels and ARGOS satellite big data together with other in situ heterogeneous sources. Furthermore, data mining services are being developed for knowledge extraction on species habitats and temporal behaviour trends. Also, high level fusion and reasoning services which process big data observations are deployed to forecast marine wildlife behaviour with estimated uncertainties. These will be tested and demonstrated under targeted thematic scenarios in EO4wildlife using a Big Data platform a cloud resources.

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Metadaten
Titel
EO Big Data Connectors and Analytics for Understanding the Effects of Climate Change on Migratory Trends of Marine Wildlife
verfasst von
Z. A. Sabeur
G. Correndo
G. Veres
B. Arbab-Zavar
J. Lorenzo
T. Habib
A. Haugommard
F. Martin
J.-M. Zigna
G. Weller
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-89935-0_8