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Istituto sull'inquinamento atmosferico

Torna all'elenco Contributi in rivista anno 2017

Contributo in rivista

Tipo: Articolo in rivista

Titolo: Exploring the depths of the global earth observation system of systems

Anno di pubblicazione: 2017

Formato: Elettronico Cartaceo

Autori: Max Craglia, Jiri Hradec, Stefano Nativi, Mattia Santoro

Affiliazioni autori: EC-JRC, EC-JRC, CNR-IIA, CNR-IIA

Autori CNR:

  • STEFANO NATIVI
  • MATTIA SANTORO

Lingua: inglese

Abstract: Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data, especially for managing satellite time series. These infrastructures build on the concept of multidimensional data model (data hypercube) and are complex systems engaging different disciplines and expertise. For this reason, their interoperability capacity has become a challenge in the Global Change and Earth System science domains. To address this challenge, there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features. In this respect, a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain. This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas, with the final goal of enabling and facilitating interoperability. It introduces six modeling views, each of them is described according to: its main concerns, principal stakeholders, and possible patterns to be used. The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional "cubes" along with the more recent and analogous development in the Earth Observation domain, and puts forward a set of interoperability recommendations based on the modeling views.

Lingua abstract: inglese

Pagine da: 21

Pagine a: 46

Rivista:

Big earth data Taylor & Francis
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 2574-5417

Numero volume: 1

Numero fascicolo: 1-2

DOI: 10.1080/20964471.2017.1401284

Referee: Sė: Internazionale

Stato della pubblicazione: Published version

Parole chiave:

  • Machine learning
  • GEOSS
  • data management
  • neural networks
  • word embedding

URL: http://www.tandfonline.com/doi/abs/10.1080/20964471.2017.1401284

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