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BigFastData

Financiado

Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorith...

This proposal addresses a pressing need from emerging big data applications such as genomics and data center monitoring: besides the scale of processing, big data systems must also enable perpetual, low-latency processing for a br... ver más

31/12/2023

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ECOLE POLYTECHNIQUE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-12-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2016-COG: ERC Consolidator Grant

I+D

Cerrada hace 8 años

0% 100% 100%

2 Participantes

2.47M€ | Lider

STABLEPLASTIC

347.20K€ | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 78 meses Fecha Inicio: 2017-06-15
Fecha Fin: 2023-12-31

Líder del proyecto

ECOLE POLYTECHNIQUE

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto This proposal addresses a pressing need from emerging big data applications such as genomics and data center monitoring: besides the scale of processing, big data systems must also enable perpetual, low-latency processing for a broad set of analytical tasks, referred to as big and fast data analysis. Today’s technology falls severely short for such needs due to the lack of support of complex analytics with scale, low latency, and strong guarantees of user performance requirements. To bridge the gap, this proposal tackles a grand challenge: How do we design an algorithmic foundation that enables the development of all necessary pillars of big and fast data analysis? This proposal considers three pillars: 1) Parallelism: There is a fundamental tension between data parallelism (for scale) and pipeline parallelism (for low latency). We propose new approaches based on intelligent use of memory and workload properties to integrate both forms of parallelism. 2) Analytics: The literature lacks a large body of algorithms for critical order-related analytics to be run under data and pipeline parallelism. We propose new algorithmic frameworks to enable such analytics. 3) Optimization: To run analytics, today's big data systems are best effort only. We transform such systems into a principled optimization framework that suits the new characteristics of big data infrastructure and adapts to meet user performance requirements. The scale and complexity of the proposed algorithm design makes this project high-risk, at the same time, high-gain: it will lay a solid foundation for big and fast data analysis, enabling a new integrated parallel processing paradigm, algorithms for critical order-related analytics, and a principled optimizer with strong performance guarantees. It will also broadly enable accelerated information discovery in emerging domains such as genomics, as well as economic benefits of early, well-informed decisions and reduced user payments.

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