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Scalable big data platform, mining and analytics services for optimized forecast of animals habitats

Scalable big data platform, mining and analytics services for optimized forecast of animals habitats
Scalable big data platform, mining and analytics services for optimized forecast of animals habitats
The effects of climate change have been observed for decades now that we can access to multiple methods of Earth Observation (EO) using in situ, air-borne and space-borne sensing. The generated EO Big Data from these sources is of paramount importance for scientists to understand the effects of climate change and the specific engendered natural (and anthropogenic) processes that are
likely to trigger the changing behaviour of species on Earth. In the EO4wildlife project (http://www.copernicus.eu/projects/eo4wildlife), we have access to Copernicus and Argos EO Big Data for investigating the changes of habitats for a variety of marine species. The challenge is to forecast the habitats by identifying the causal relationships between animal presence and Metocean environmental
fronts. This is achieved by processing data of animal presence, which are relatively small in size and sparse, and their correlation with environmental datasets, which are large and dense in feature space. This poses big data challenges in terms of optimisation of resources, mining and feature selections.
Once overcome, it improves the performance of the forecasting models. The availability of big geospatial information, satellite data and in situ observations enabled us experiment on the scalability of our distributed data storage technologies and analytics services in the cloud. We specifically
deployed cluster infrastructure via Spark for a resilient distribution of processing over multiple nodes. The testbed experiments of our big data processing performance are validated under three types of selected habitat forecasting workflows.
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Castel, F.
cdd27192-6603-44f4-a6d2-c30cc06340ec
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Castel, F.
cdd27192-6603-44f4-a6d2-c30cc06340ec
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b

Sabeur, Zoheir, Correndo, Gianluca, Castel, F., Neumann, Geoffrey, Veres, Galina and Arbab-Zavar, Banafshe (2018) Scalable big data platform, mining and analytics services for optimized forecast of animals habitats. At International Congress on Environmental Modelling and Software: Modelling for Sustainable Food-Energy-Water Systems (28/06/18) International Congress on Environmental Modelling and Software: Modelling for Sustainable Food-Energy-Water Systems, Fort Collins, United States. 24 - 28 Jun 2018.

Record type: Conference or Workshop Item (Paper)

Abstract

The effects of climate change have been observed for decades now that we can access to multiple methods of Earth Observation (EO) using in situ, air-borne and space-borne sensing. The generated EO Big Data from these sources is of paramount importance for scientists to understand the effects of climate change and the specific engendered natural (and anthropogenic) processes that are
likely to trigger the changing behaviour of species on Earth. In the EO4wildlife project (http://www.copernicus.eu/projects/eo4wildlife), we have access to Copernicus and Argos EO Big Data for investigating the changes of habitats for a variety of marine species. The challenge is to forecast the habitats by identifying the causal relationships between animal presence and Metocean environmental
fronts. This is achieved by processing data of animal presence, which are relatively small in size and sparse, and their correlation with environmental datasets, which are large and dense in feature space. This poses big data challenges in terms of optimisation of resources, mining and feature selections.
Once overcome, it improves the performance of the forecasting models. The availability of big geospatial information, satellite data and in situ observations enabled us experiment on the scalability of our distributed data storage technologies and analytics services in the cloud. We specifically
deployed cluster infrastructure via Spark for a resilient distribution of processing over multiple nodes. The testbed experiments of our big data processing performance are validated under three types of selected habitat forecasting workflows.

Text Scalable Big Data Platform Miningand Analytics Services for Opt - Accepted Manuscript
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More information

Accepted/In Press date: 15 May 2018
Published date: 24 June 2018
Additional Information: All papers will be published online in the Digital Commons publication system as proceedings from the iEMSs 2018 conference. The conference proceedings will receive an ISBN. You can access the proceedings from the society website at: http://www.iemss.org
Venue - Dates: International Congress on Environmental Modelling and Software: Modelling for Sustainable Food-Energy-Water Systems, Fort Collins, United States, 2018-06-24 - 2018-06-28

Identifiers

Local EPrints ID: 421909
URI: https://eprints.soton.ac.uk/id/eprint/421909
PURE UUID: 23c94f3b-7e35-4c33-82f9-659e02f1225e
ORCID for Zoheir Sabeur: ORCID iD orcid.org/0000-0003-4325-4871
ORCID for Gianluca Correndo: ORCID iD orcid.org/0000-0003-3335-5759

Catalogue record

Date deposited: 09 Jul 2018 16:30
Last modified: 06 Oct 2018 00:34

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