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Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics

Objective

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.
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Coordinator

UNIVERSIDAD POLITECNICA DE MADRID

Address

Calle Ramiro De Maeztu 7 Edificio Rectorado
28040 Madrid

Spain

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 484 360

Administrative Contact

Marta Patiño Martinez (Prof.)

Participants (11)

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ATOS SPAIN SA

Spain

EU Contribution

€ 303 450

CA TECHNOLOGIES DEVELOPMENT SPAIN SA

Spain

EU Contribution

€ 418 800

LEANXCALE SL

Spain

EU Contribution

€ 415 321

IDRYMA TECHNOLOGIAS KAI EREVNAS

Greece

EU Contribution

€ 575 125

INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS

Greece

EU Contribution

€ 300 800

INTEL RESEARCH AND INNOVATION IRELAND LIMITED

Ireland

EU Contribution

€ 174 614

INTEL RESEARCH AND DEVELOPMENT IRELAND LIMITED

Ireland

EU Contribution

€ 237 538

SYNC LAB SRL

Italy

EU Contribution

€ 381 800

ALTICE LABS SA

Portugal

EU Contribution

€ 106 892

MEO-SERVICOS DE COMUNICACOES E MULTIMEDIA SA

Portugal

EU Contribution

€ 107 308

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA

Portugal

EU Contribution

€ 398 992

Project information

Grant agreement ID: 619606

Status

Closed project

  • Start date

    1 February 2014

  • End date

    31 January 2017

Funded under:

FP7-ICT

  • Overall budget:

    € 6 023 777

  • EU contribution

    € 3 905 000

Coordinated by:

UNIVERSIDAD POLITECNICA DE MADRID

Spain

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