EDF2012 Nuria de Lama - BIG


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  • We cannot do it alone To dimension our place holder we need the help of the 8 Usage area projects. … but we aware we will not limit to them. We are also open to others. We are open to Celtic…Itea, the ETP, Net!works…
  • Our architrute and a first glimpse of what we do
  • EDF2012 Nuria de Lama - BIG

    1. 1. BIG 07/06/2012 Nuria de Lama(Big Data Public Private Forum) European Data Forum Copenhagen, 7th June 2012 Nuria de Lama Representative of Atos Research & Innovation to the EC 1
    2. 2. 07/06/2012 Nuria de LamaTable of Contents1. Motivation 1. Example (company level) 2. Business potential2. Learning experience: Future Internet3. BIG: Big Data Public Private Forum2 2
    3. 3. 07/06/2012 Nuria de LamaTable of Contents1. Motivation 1. Example (company level) 2. Business potential2. Learning experience: Future Internet3. BIG: Big Data Public Private Forum3 3
    4. 4. A use case in Atos: Olympic Games 07/06/2012 Nuria de Lama(increasing demand of data processing, storageand innovative applications) 4
    5. 5. Business potential of openness and 07/06/2012 Nuria de Lamacollaboration 5
    6. 6. 07/06/2012 Nuria de LamaTable of Contents1. Motivation 1. Example (company level) 2. Business potential▶ Learning experience: Future Internet▶ BIG: Big Data Public Private Forum6 6
    7. 7. 07/06/2012The FI PPP: Towards an Nuria de Lamainnovation landscape7 7
    8. 8. 07/06/2012FI PPP Programme Implementation: Nuria de LamaTechnology aligned with needs8 8
    9. 9. 07/06/2012FI-WARE: Some figures and data Nuria de Lama Main data 26 partners 5 Universities 4248 Person Months (excl. open calls) Total Funding 41 M€ Open calls 12,3 M€ Total budget 66,4 M€ Three years duration 9
    10. 10. 07/06/2012FI-WARE: Collaboration with Nuria de LamaUsage Areas 10
    11. 11. 07/06/2012FI Core Platform Architecture: Nuria de Lamamain chapters 11
    12. 12. 07/06/2012FI-WARE and Big Data Nuria de Lama▶ The FI-WARE project addresses, among others, the following Data Management topics: – Publish/ Subscription – Complex Even Processing (CEP) – Multimedia analysis – Unstructured data analysis – Meta-data pre-processing – Semantic annotation and application support – Big Data analysis, based on • Hadoop (heterogeneous MapReduce platform mainly used for ad-hoc data exploration of large sets of data.) • MongoDB: a scalable, high-performance, open source, document-oriented database. • SAMSON Platform: high-performance streaming MapReduce platform that is used for the near-real time analysis of streaming data. 12
    13. 13. 07/06/2012 Nuria de LamaTable of Contents1. Motivation 1. Example (company level) 2. Business potential▶ Learning experience: Future Internet▶ BIG: Big Data Public Private Forum13 13
    14. 14. 07/06/2012BIG: Key facts Nuria de Lama▶ Type of project: Coordination Action (CA)▶ Duration: 24 months▶ Budget: 3,055 Meuro▶ Funding: 2,5 Meuro▶ Consortium: 11 partners Overall objective Address technical, business and policy aspects of IIM and Big Data with the aims of shaping the future of the area, positioning it in H2020 and bringing the necessary stakeholders into a self- sustainable industrially-led initiative to enhance EU competitiveness taking full advantage of Big Data. 14
    15. 15. 07/06/2012Project objectives Nuria de Lama▶ Main Missions 1. Build a self-sustainable Industrial community around Big Data in Europe • Technical level establishing the proper channels to gather information • industrially-led initiative to influence adequately the decision makers 2. Promote adoption of earlier waves of big data technology 3. Tackle adequately existing barriers such as policy and regulation issues▶ Concrete Objectives (and outputs from BIG project) ▶ Define Stakeholders and players in the value chain (D2.3 Sector’s Requisites). ▶ Elaborate a clear picture of existing technological trends and their maturity (D2.2 Technical white papers ) ▶ Acquire a sharp understanding of how big data can be applied to concrete environments/sectors (D2. 4 Sector’s Roadmap ) ▶ Disseminate results and involve different stakeholders (D3.4 Project Dissemination Reports and D3.5 Stakeholder engagement activities) ▶ Define priorities based on expected impact (D.2.5 Integrated Roadmap ) ▶ Contribute to EU competitiveness and position it in Horizon 2020 (D4.2 IPR, Standardization Recommendations) 15
    16. 16. 07/06/2012BIG: approach (I) Nuria de Lama 16
    17. 17. 07/06/2012BIG: approach (II) Nuria de Lama EIP, Digital Agenda, EU Directives, BIG Objective: Enable Economic Drivers… and enhance mapping between TD and BU issues FP7 Projects, WG inputs, Research agenda… 17
    18. 18. 07/06/2012BIG: major activities Nuria de Lama Technology state of art and sector analysis Definition of the proposed application Assess the impact/applicability of the sectors different technologies Roadmapping activity Individual roadmap elaboration Roadmap consolidation (per sector) (cross-sectorial) Big Data Public Private Forum Impact Assessment Sustainability Towards Horizon 2020 Big Data initiavive definition 18
    19. 19. 07/06/2012BIG: methodology Nuria de LamaWhat is there (in Which is the level Which support Which metrics Which are theterms of of maturity? actions are should be used? impacts ?technology)? needed? Can it be Which is the actual What are theWhat is needed implemented? How can it be situation? residual(domain done? challenges?requirements)? What is the time What do we want to to market? When can it be achieve? Highlight barriers,What benefits will done? strengths, futureit bring to the directions…stakeholders? Is there any kind How to fulfill the of restrictions? Links between existing gap topics (technology/ (cost/timeframe…)? sectors needs)? 19
    20. 20. 07/06/2012BIG: project structure Nuria de Lama Industry driven working groups Manufacturing, Finance & Telco, Media & Health Public Sector Retail, Energy, insurance Entertainment Transport Needs Supply Value Chain Data Data Data Data Data acquisition analysis curation storage usage • Data preprocessing • Structured data • Decision support • Semantic analysis • Trust • Unstructured Data • RDBMS limitations • Decision making • Sentiment analysis • Provenance • Event processing • NOSQL • Automatic steps • Other features • Data augmentation • Sensors networks • Cloud storage • Domain-specific analysis • Data validation • Streams usage • Data correlation Technical areas 20
    21. 21. Atos Research & InnovationNuria de LamaThank youFor more information please contact:Ph: + 34 91214 9321nuria.delama@atosresearch.euAtos, the Atos logo, Atos Consulting, Atos Worldline, Atos Sphere, AtosCloud and Atos WorldGridare registered trademarks of Atos SA. June 2011© 2011 Atos. Confidential information owned by Atos, to be used bythe recipient only. This document, or any part of it, may not bereproduced, copied, circulated and/or distributed nor quoted withoutprior written approval from Atos. 09/06/12