CINI Big Data Lab Università degli Studi di Bari Aldo Moro Dip. Informatica

Transcription

1 PPP Big Data Value: ruolo e finalità Donato Malerba CINI Big Data Lab Università degli Studi di Bari Aldo Moro Dip. Informatica Big Data Entering the black-box: research challenges, application and business practices Politecnico di Torino, 27 Aprile

2 Commission urges governments to embrace potential of Big Data Data collection and exploitation is a growing phenomenon. In response to industry and fundamental demands the European Commission has called on national governments to wake-up to this big data revolution. European Commission Towards a thriving data-driven economy 14 th July

3 Commission urges governments to embrace potential of Big Data The European digital economy has been slow in embracing the data revolution compared to the USA and also lacks comparable industrial capability. Neelie Kroes (European Commissioner for the Digital Agenda) "Data is not scary, or intrusive. With the right legal protection and anonymisation tools data is the fuel which lays the foundation of a new economy. Giving every kind of organisation the building blocks to boost productivity and performance, from farm to factory, from the lab to the shop floor, that's what Europe needs. 3

4 Where are the obstacles? The main problems identified in public consultations on big data are: Lack of cross-border coordination Insufficient infrastructure and funding opportunities A shortage of data experts and related skills Fragmented and overly complex legal environment 4

5 Main concrete actions proposed to solve these problems A Big Data public-private partnership that funds game-changing big data ideas, in areas such as personalised medicine and food logistics. Create an open data incubator (within the Horizon 2020 framework), to help SMEs set up supply chains based on data and use cloud computing more. 5

6 Main concrete actions proposed to solve these problems Propose new rules on "data ownership" Mapping of data standards, identifying potential gaps Establish a series of Supercomputing Centres of Excellence to increase number of skilled data workers in Europe Create network of data processing facilities in different Member States 6

7 Private-Public Public Partnership: Why To solve problems together with industry To strengthen industrial European leadership To strongly commit industry to joint objectives To facilitate prioritisation of R&I in line with the Europe 2020 objectives and industry needs Two types of PPPs Contractual PPPs (cppps) Joint Technology Initiatives 7

8 Contractual Private-Public Public Partnerships (cppps) Implement industry-defined strategic research and innovation agendas through co-funded projects selected through H2020 calls for proposals Budget is only committed on an annual basis in Work Programs (WPs) Calls are prepared on the basis of an industry-developed multi-annual roadmap Commitments over a seven year period for both EU and the industrial partners EU investments Industrial partners should leverage the direct EU investments generating additional investments (private investments 3 to 10 times the level of public investments) 8

9 Big Data Value cppp cppps take the form of contractual arrangements between the European Commission and representative industrial associations. The European Commission and Europe s data industry have committed to invest 2.5 billion in the Big Data Value cppp that aims to strengthen the data sector and put Europe at the forefront of the global data race. The EU has allocated over 500 million of investment over 5 years ( ) from H2020 Private partners are expected to leverage this through sector investments of four times the cppp budget (ie 2 billion) 9

10 What instruments for the cppp? All instruments that are foreseen for the Industrial Leadership part of H2020 Research & Innovation Actions (RIAs) Innovation Actions (IAs) Coordination and Support Actions (CSAs) 10

11 What is the role of BDVA in the cppp? The Big Data Value Association represents Private side of the cppp Main role of BDVA: providing the Big Data Value strategic agenda (SRIA) and its regular updates; Defining and monitoring the metrics of the cppp; Joining the European Commission in the cppp partership board. 11

12 12 Big Data Value Association

13 13 Big Data Value Association

14 14 Big Data Value Association

15 15 Big Data Value Association

16 BDVA Board of Directors BDVA full and associated members (Dec 2015) President: Jürgen Müller, SAP Vice-President: Jan Sundelin, TIE Kinetix Vice-President: José María Cavanillas, ATOS 1. Sören Auer Fraunhofer 2. Tonny Velin Answare 3. Bjorn Skjellaug Sintef 4. Walter Waterfeld Software AG 5. Catherine Simon Thales 6. Ernestina Menasalvas UPM 7. David Bernstein IBM 8. Joe Butler Intel 9. Klaus Pohl Paluno 10. Valere Robin Orange 11. Tuomo Tuikka VTT 12. Yannis Kliafas ATC 13. Jan Sundelin TIE Kinetix 14. Jürgen Müller SAP Board of Directors (BDVA full members) 15. Jose Maria Cavanillas Atos 16. Daniel Saez ITI 17. Robert Seidel Nokia 18. Thomas Hahn Siemens 19. Colin Upstill IT Innovation 20. Jose Luis Angoso Indra 21. Edward Curry Insight (Deri) 22. Volker Markl DFKI 23. Dario Avallone Engineering 24. Donato Malerba CINI Secretary General : Deputy Sec. Generals: Stuart Campbell, TIE Kinetix Nuria De Lama, ATOS & Julie Marguerite, THALES 16

17 17 Big Data Value Association

18 How is participation in cppp projects determined? Proposals for projects have to be submitted following European Commission calls for proposal. The European Commission will decide on the final Work Programme content and check eligibility and select the projects with the support of external evaluators according to the H2020 evaluation criteria and guidelines. BDVA will not take part in decision making. 18

19 How is participation in cppp projects determined? 1. BDVA provides SRIA 2. The Commission is in charge of defining the official Work Programme 3. Proposals are submitted to the various calls 4. Appropriate proposals are selected with the support of external experts Contractual Arrangement Big Data Value Association Industry driven Defines Research and Innovation Agenda Specifies Key Performance Indicators Industry Large Industry SME EU Commission Association proposes research objectives Stakeholder Community Stakeholders Grant Agreement per project Big Data Projects within Horizon 2020 Projects running within Expected Total Funding ~ 500 MEUR Research User 19

20 BDV PPP/A - Commitments Leverage the cppp investments through sector investments of 4 times Open, transparent and inclusive definition Update Strategic Research & Innovation Agenda (SRIA); Ensure 20% SME participating organisations; Support to the ex-post assessment of the implemented projects; Leverage the achieved results in the market Develop skills and competences in Big Data Value Actively involve all relevant sector players, Work with others for alignment of goals and ensure synergies; Governance model, which supports openness and efficiency Monitoring Impact 20

21 BDV A/PPP Benefits Europe: Public / Private cooperation and coordination Developing strategic goals of BDV research Improving Industrial Competitiveness of Europe Promoting BDV Offering in organisations Contributing to policy, education etc You: Networking with peers (Proposals, Projects..) Influencing others including EU (eg Work Program input) Working with other on technical matters, societal Designing the over all BDV PPP ecosystem 21

22 Indicative Timeline October Signing the Contractual Arrangement between European Commission and the Big Data Value Association 2015 Refinement of the Big Data Value Strategic Research and Innovation Agenda Continuing stakeholder community building 2016 Start of first Big Data Value PPP projects within H PPP projects ending 22

23 BDVA Currently Members All paying, in 9 months 2/3 Full, 1/3 Associate 50:50 Industry/Academic Open but Statutes/bylaws Vision Contribution BUT Being in BDVA <> Awarded Project not an evaluation Criteria!

24 BDVA Founding Members 1. AnswareTech s.l. 2. Athens Technology Center S.A. (ATC) 3. Atos SE 4. Consorzio Interuniversitario Nazionale per l'lnformatica (CINI) 5. Engineering Ingegneria Informatica SpA 6. Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. German 7. German Research Center for Artificial Intelligence (DFKI GmbH) 8. IBM Israel Science and technology Ltd 9. INDRA Sistemas S.A 10. Instituto Tecnológico de Informática (ITI) 11. Intel Research and Innovation Ireland Ltd 12. IT Innovation Center 13. National University of Ireland Galway 14. Nokia Solutions and Networks Oy 15. Orange SA 16. SAP SE 17. Siemens AG 18. Software AG 19. Stiftelsen SINTEF 20. THALES Communications and Security S.A.S 21. TIE Kinetix N.V. 22. Universidad Politécnica de Madrid 23. Universität Duisburg Essen (Paluno) 24. VTT Technical Research Centre of Finland 24

25 Main components and research priorities in the SRIA Innovation Spaces serve as hubs for bringing the technology and application developments together and cater for the development of skills, competence, and best practices. Improving understanding of data by deep analytics (e.g. predictive modelling, graph mining,...) Architectures for analysing data including real-time data (e.g. recommendation engines,...) Visualization and user experience (e.g. User adaptive systems, search capabilities,...) Lighthouse Projects Large scale demonstrations focusing on certain sectors and domains Data management engineering (e.g. Data integration, data integrity,...) Privacy and anonymisation mechanisms 25 Five Thematic Priorities

26 European Innovation Spaces a core component of the cppp European i-spaces Serve as hubs for bringing the technology and application developments together and cater for the development of skills, competence, and best practices. Offer new and existing technologies and tools from industry and open source software initiatives as a basic service Facilitate the access to data assets. Allow an interdisciplinary approach along the various dimensions technology, applications, legal, social, and business, data assets and the building up of skills. 26 Skills Social People Legal Data Business Testing Piloting Application <date > Technical

27 Lighthouse projects a mechanism for large-scale demos and awareness Lighthouse Projects The major mechanism for Europe to demonstrate Big Data Value ecosystems and sustainable data marketplaces Running data-driven large scale demonstrations Propose replicable solutions by using existing technologies or very near to market technologies that could be integrated in an innovative way and show evidence of data value Create high level impact and broadcast visibility and awareness driving towards faster uptake of Big Data Value applications and solutions 27

28 H2020 WP H2020 Work Programme 2016/ / Lighthouse Project 1 Lighthouse Projects Lighthouse Project 2 European Innovation Spaces (i- Spaces) Establishing Baseline Application of lessons learnt Project 3 Further improvement R&I and Innovation Projects in specific Technology Areas and Applications in various Sectors/domains 28 CSA Stakeholder Platform, Roadmapping, Governance, CSA EIS/E support, Benchmarking, CSA Legal, Social, Skills, CSA Business, Ecosystems

29 H2020 WP Horizon Work Programme 2016/ / Light house projects Personalised Medicine / Manufacturing Logistics/ Media Energy European Innovation Space Establishing Baseline Application of Lesson Learnt Further Improvement R&I and Innovation Projects in Specific Technology Areas and Applications in Various Sectors/Domains CSA Stakeholder Platform, Road- Mapping, Governance CSA EIS/E Support, Benchmarking CSA Legal, Social, Skills CSA Business, Ecosystems 29

30 Task Forces 30 TF1: Programme: Contributing to the H2020 Programme content of the BDV PPP TF2: Impact: Maintain the various KPIs defining the expected Impact of BDV PPP TF3: Community: Big data community engagement and participation TF4: Communication: Communication plan for creating awareness around the BDVA TF5: Legal: Bridge Big Data technology with legal and policy matters TF6: Technical: Identifying and refining the technical challenges of the programme eg Data Management, Data Protection... TF7: Application: Domain usage group which can influence others eg Telecoms TF8: Business: Examining the business and economic influences and business areas TF9: Societal: Examining the societal impact on business, citizens TF10: Skills and Education: What skills are needed for the next knowledge workers TF0: Administrative and strategic activities requested by BDVA GA/BOD

31 Task Forces 31 TF1: Programme: Contributing to the H2020 Programme content of the BDV PPP TF2: Impact: Maintain the various KPIs defining the expected Impact of BDV PPP TF3: Community: Big data community engagement and participation TF4: Communication: Communication plan for creating awareness around the BDVA TF5: Legal: Bridge Big Data technology with legal and policy matters TF6: Technical: Identifying and refining the technical challenges of the programme eg Data Management, Data Protection... TF7: Application: Domain usage group which can influence others eg Telecoms TF8: Business: Examining the business and economic influences and business areas TF9: Societal: Examining the societal impact on business, citizens TF10: Skills and Education: What skills are needed for the next knowledge workers TF0: Administrative and strategic activities requested by BDVA GA/BOD

32 Further information The Strategic Agenda and further documents are available on: Credit: part of this presentation is courtesy of Stuart Campbell, SG of BDVA. 32

33 The CINI Consortium ( cini.it) CINI (Consorzio Interuniversitario Nazionale per l Informatica) A consortium of 42 Italian public universities that deliver MS degrees in CE and/or CS Established in professors of both Computer Science and Computer Engineering 33

34 International positioning CINI Role in H2020: coordinating the overall Italian IT community to support the Italian Ministries in various project steps Member of the Networked European Software and Services Initiative (NESSI); Founding member of the Big Data Value AISBL 34

35 Cini Lab on Big Data CINI is currently equipped with 6 networkstructured Labs, distributed throughout the whole national territory. The CINI Lab on "Big Data : 32 Nodes About 300 permanent researchers/professors 35

36 Cini Lab on Big Data METHODOLOGIES Data Modeling Information Extraction Information integration Querying and retrieval Mining and analytics Open data Algorithms for data intensive scalable computing Distributed computing and architectures Privacy and security TECHNOLOGIES Distributed computing and architectures Toolkits for Big Data Sources Open Data Portals APPLICATIONS Scientific applications of Big Data Social applications of Big Data Big Data for Enterprise and Government Big Data for Security 36