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QuAnGIS

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Cerrado

Question based Analysis of Geographic Information with Semantic Queries

"In the age of big data, geographic information has become a central means for data scientists of various disciplines to embed their analysis into a spatio-temporal context, from human mobility patterns and social inequality to th... ver más

31/08/2024

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

UNIVERSITEIT UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-08-31

ERC-2018-STG: ERC Starting Grant

I+D

Cerrada hace 7 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

1.50M€ | Lider

JEANOLOGIA

461.53K€ | Participante

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Duración del proyecto: 69 meses Fecha Inicio: 2018-11-19
Fecha Fin: 2024-08-31

Líder del proyecto

UNIVERSITEIT UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "In the age of big data, geographic information has become a central means for data scientists of various disciplines to embed their analysis into a spatio-temporal context, from human mobility patterns and social inequality to the investigation of personal health. However, as the variety of data sources available on the Web increases, it becomes more and more impossible to comprehend and utilize all tools available to answer geo-analytical questions. The variety of formats and syntaxes required by Geographic Information System (GIS) toolboxes or statistical packages divides the research community into various tool expert groups. Hence, whenever a functionality is needed but not available in one tool, analysts are forced to reformulate their questions in terms of the technicalities of another tool. Furthermore, new tools are difficult to learn, and translations cause severe interoperability problems. Finally, this procedure does not scale with the increasing variety of analytic resources on the Web, preventing analysts from tapping its full potential, and making the promise of seamless big data analytics a mere distant dream. Consider, in contrast, how easy it is for a user of a digital smartphone assistant such as Amazon's Alexa to ask a question like ""What is the weather today?"" and get back an answer from the Web. It would mean a tremendous breakthrough in information science if analysts could similarly ask familiar questions in order to get the tools and data required to answer them. Unfortunately, analytic technology currently cannot handle such questions. To realize this vision, it is necessary to understand how analytic resources can be captured with the questions they answer. In this project, I will develop a novel theory of interrogative spatial concepts to turn geo-analytical questions into a machine-readable form using semantic queries. In this form, questions can directly be matched with the capacity of major analytic GIS tools and data on the Web."

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