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SemaGrow

Financiado

Cerrado

SemaGrow Data intensive techniques to boost the real time performance of global...

SemaGrow Data intensive techniques to boost the real time performance of global agricultural data infrastructures As the trend to open up data and provide them freely on the Internet intensifies, the opportunities to create added value by combining and cross-indexing heterogeneous data at a large scale increase. To seize them, we need infrast... ver más

31/10/2015

UAH

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

UNIVERSIDAD DE ALCALÁ No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 847

Fecha límite participación Sin fecha límite de participación.

Ver 7 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2015-10-31 No tenemos la información de la convocatoria

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Información adicional privada

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7 Participantes

UAH

0.00€ | Lider

FONDAZIONE WWF MEDITERRANEAN

N.D. | Participante

Institute of Physics Belgrad...

N.D. | Participante

SWC

N.D. | Participante

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Líder del proyecto

UNIVERSIDAD DE ALCALÁ No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 847

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto As the trend to open up data and provide them freely on the Internet intensifies, the opportunities to create added value by combining and cross-indexing heterogeneous data at a large scale increase. To seize them, we need infrastructure that is not only efficient, real-time responsive and scalable but is also flexible and robust enough to welcome data in any schema and form and to transparently relegate and translate queries from a unifying end-point to the multitude of data services that make up the open data cloud.This, relies on detailed and accurate data summaries and other data source annotations, and with increased data volumes and heterogeneity managing these annotations, it becomes by itself a challenging data problem. SemaGrow will (a) develop scalable and robust semantic storage and indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; (b) develop query decomposition, source selection, and distributed querying methods that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation; and (c) rigorously test its components and overall architecture over real, complex, interconnected datasets comprising data and document collections, sensor data, and GIS data.SemaGrow will be rigorously tested on the large-scale and complex agricultural data service ecosystem, comprising more than 20 currently operating data services providing today Gigatriples of RDF data, projected to double before SemaGrow ends and to reach Teratriples by 2020. Being able to query across these datasets is a real and present need. SemaGrow envisages to develop the scalable, efficient, and robust data services needed to take full advantage of the data-intensive and inter-disciplinary Science of 2020 and to re-shape the way that data analysis techniques are applied to the heterogeneous data cloud.

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