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EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife

EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife
EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife
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 wild-life behaviour with estimated uncertainties. These will be tested and demonstrated under targeted thematic scenarios in EO4wildlife using a Big Data platform a cloud resources.
1868-4238
85-94
Springer
Sabeur, Z.A.
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Correndo, G.
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Veres, G.
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Arbab-Zavar, B.
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Lorenzo, J.
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Habib, T.
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Haugommard, A.
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Martin, F.
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Zigna, J.-M.
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Weller, G.
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Hřebíček,, Jiří
Denzer, Ralf
Schimak, Gerald
Pitner, Tomáš
Sabeur, Z.A.
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Correndo, G.
fea0843a-6d4a-4136-8784-0d023fcde3e2
Veres, G.
3c2a37d2-3904-43ce-b0cf-006f62b87337
Arbab-Zavar, B.
40e175ea-6557-47c6-b759-318d7e24984b
Lorenzo, J.
5f3929b3-c3ce-47af-977b-17e8db42baed
Habib, T.
b958478a-f8fd-4f70-9240-e8c18934406a
Haugommard, A.
3973a9a5-ec63-424f-8883-19c49bc2ba26
Martin, F.
eb60e024-b819-4329-844d-34d8f34e471a
Zigna, J.-M.
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Weller, G.
a68ede4b-bcef-4f1a-8fca-833938f11c80
Hřebíček,, Jiří
Denzer, Ralf
Schimak, Gerald
Pitner, Tomáš

Sabeur, Z.A., Correndo, G., Veres, G., Arbab-Zavar, B., Lorenzo, J., Habib, T., Haugommard, A., Martin, F., Zigna, J.-M. and Weller, G. (2017) EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife. Hřebíček,, Jiří, Denzer, Ralf, Schimak, Gerald and Pitner, Tomáš (eds.) In Environmental Software Systems. Computer Science for Environmental Protection: 12th IFIP WG 5.11 International Symposium, ISESS 2017, Zadar, Croatia, May 10-12, 2017, Proceedings. vol. 507, Springer. pp. 85-94 . (doi:10.1007/978-3-319-89935-0_8).

Record type: Conference or Workshop Item (Paper)

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 wild-life 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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EO_Big_Data_Connectors_and_Analytics_for_Understanding_the_Effects_of_Climate_Change_on_Migratory_Trends_of_Marine_Wildlife - Accepted Manuscript
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Accepted/In Press date: 29 January 2017
e-pub ahead of print date: 24 April 2017
Published date: May 2017
Venue - Dates: International Symposium on Environmental Software Systems, Zadar, Croatia, 2017-05-10 - 2017-05-12

Identifiers

Local EPrints ID: 412614
URI: https://eprints.soton.ac.uk/id/eprint/412614
ISSN: 1868-4238
PURE UUID: eeb34023-e491-46ea-b380-b71b81905051
ORCID for Z.A. Sabeur: ORCID iD orcid.org/0000-0003-4325-4871
ORCID for G. Correndo: ORCID iD orcid.org/0000-0003-3335-5759

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Date deposited: 24 Jul 2017 16:32
Last modified: 14 Mar 2019 01:41

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Contributors

Author: Z.A. Sabeur ORCID iD
Author: G. Correndo ORCID iD
Author: G. Veres
Author: B. Arbab-Zavar
Author: J. Lorenzo
Author: T. Habib
Author: A. Haugommard
Author: F. Martin
Author: J.-M. Zigna
Author: G. Weller
Editor: Jiří Hřebíček,
Editor: Ralf Denzer
Editor: Gerald Schimak
Editor: Tomáš Pitner

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