Observatorio de I+D+i UPM

Memorias de investigación
Proyecto de I+D+i:
LeanBigData: Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics
Año:2014
Áreas de investigación
Datos
Descripción

LeanBigData aims at addressing three open challenges in big data analytics: 1) The cost, in terms of resources, of scaling big data analytics for streaming and static data sources; 2) The lack of integration of existing big data management technologies and their high response time; 3) The insufficient end-user support leading to extremely lengthy big data analysis cycles. LeanBigData will address these challenges by:

  • Architecting and developing three resource-efficient Big Data management systems typically involved in Big Data processing: a novel transactional NoSQL key-value data store, a distributed complex event processing (CEP) system, and a distributed SQL query engine. We will achieve at least one order of magnitude in efficiency by removing overheads at all levels of the big-data analytics stack and we will take into account technology trends in multicore technologies and non-volatile memories.
  • Providing an integrated big data platform with these three main technologies used for big data, NoSQL, SQL, and Streaming/CEP that will improve response time for unified analytics over multiple sources and large amounts of data avoiding the inefficiencies and delays introduced by existing extract-transfer-load approaches. To achieve this we will use fine-grain intra-query and intra-operator parallelism that will lead to sub-second response times.
  • Supporting an end-to-end big data analytics solution removing the four main sources of delays in data analysis cycles by using: 1) automated discovery of anomalies and root cause analysis; 2) incremental visualization of long analytical queries; 3) drag-and-drop declarative composition of visualizations; and 4) efficient manipulation of visualizations through hand gestures over 3D/holographic views.Finally, LeanBigData will demonstrate these results in a cluster with 1,000 cores in four real industrial use cases with real data, paving the way for deployment in the context of realistic business processes.
Internacional
Si
Tipo de proyecto
Proyectos y convenios en convocatorias públicas competitivas
Entidad financiadora
Nacionalidad Entidad
Sin nacionalidad
Tamaño de la entidad
Fecha concesión
11/02/2014
Participantes
  • Participante: Carlos Sanchez Marín (UPM)
  • Director: Marta Patiño Martinez (UPM)
  • Participante: Luis Mengual Galan (UPM)
  • Participante: Tonghong Li (UPM)
  • Participante: Valerio Vianello . (UPM)
  • Participante: Alejandra Moore . (UPM)
  • Participante: David Jiménez Peris (UPM)
  • Participante: Jose Ernesto Jimenez Merino (UPM)
  • Participante: Ivan Brondino (UPM)
  • Participante: Ricardo Jimenez Peris (UPM)
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