March 2, 2015 Journal article Open Access

Baumann, P.; Mazzetti, P.; Ungar, J.; Barbera, R.; Barboni, D.; Beccati, A.; Bigagli, L.; Boldrini, E.; Bruno, R.; Calanducci, A.; Campalani, P.; Clements, O.; Dumitru, A.; Grant, M.; Herzig, P.; Kakaletris, G.; Laxton, J.; Koltsida, P.; Lipskoch, K.; Mahdiraji, A. R.; Mantovani, S.; Merticariu, V.; Messina, A.; Misev, D.; Natali, S.; Nativi, S.; Oosthoek, J.; Pappalardo, M.; Passmore, J.; Rossi, A. P.; Rundo, F.; Sen, M.; Sorbera, V.; Sullivan, D.; Torrisi, M.; Trovato, L.; Veratelli, M. G.; Wagner, S.

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  <titles>
    <title>Big Data Analytics for Earth Sciences: the EarthServer approach</title>
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  <publisher>INFN Open Access Repository</publisher>
  <publicationYear>2015</publicationYear>
  <subjects>
    <subject>big data</subject>
    <subject>Big Data Analytics</subject>
    <subject>array databases</subject>
    <subject>Earth Sciences</subject>
    <subject>interoperability</subject>
    <subject>standards</subject>
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  <dates>
    <date dateType="Issued">2015-03-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.openaccessrepository.it/record/74387</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1080/17538947.2014.1003106</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://www.openaccessrepository.it/communities/infn</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/FP7/283610/">283610</awardNumber>
      <awardTitle>European Scalable Earth Science Service Environment</awardTitle>
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