European activities in the area of Big Data

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1 Connecting Europe for New Horizon European activities in the area of Big Data Márta Nagy-Rothengass DG CONNECT, Head of Unit "Data Value Chain" META-Forum 2013, 19 September 2013, Berlin

2 OUTLINE 1. Data value chain Why does it matter? 2. Virtuous cycle of multilingual data 3. Future challenges 4. Conclusions

3 "DATA Value Chain" Vision aims at extracting the maximum value from data by building on the intelligent use of multilingual data sources across Europe. This will lead to - increased business intelligence and efficiency of private and public sectors - world class applications and services - new business opportunities involving SMEs - new ways of tackling societal challenges

4 "Data value chain" in ICT policy context Ø A strategy for smart, sustainable and inclusive growth Ø A vision to achieve high levels of employment, a low carbon economy, productivity and social cohesion, to be implemented through concrete actions at EU and national levels. Ø One of the seven flagship initiatives of Europe 2020, set out to define the key enabling role that the use of ICTs will have to play if Europe wants to succeed in its ambitions for Ø The overall aim [ ] is to deliver sustainable economic and social benefits from a digital single market [ ] Ø Support research and innovation " T h e C o m m i s s i o n i s invited to make rapid progress in key areas of the digital economy to ensure the creation of the Digital Single Market by 2015, including [ ] the availability of public sector Information." Conclusions of the European Council (4 February 2011) 4

5 Data has become a resource Types: Publicly funded data (like statistics, environmental and geographical data, meteorological data, business information, legal information), institutional and private data Use and reuse: Combination of different types of data (e.g. geo, traffic and tourism; business and open) Data activities generate externalities, positive (reuse) and negative (privacy): capture the good and avoid the bad competitive advantage is to offer the right data to the right people at the right time

6 What are the challenges at EU level? - Fostering a data-friendly legal and policy environment - Developing an efficient European data ecosystem supporting the Digital Single Market - Supporting competence in the area of multilingual data - Enhancing interoperability - Multilingualism.

7 Towards a virtuous cycle

8 Main pillars of the "Data value chain" thinking 1. Creation of "data value" friendly policy environment 2. Development of European Digital Service Infrastructure (CEF) and fostering new services in relation to - Building and reuse of language resources - Open Data portals at local, regional, national and European level 3. European Research and innovation support

9 Creation of "data value chain friendly" policy environment (1) Fostering of (Open) Data policy Adoption of the - revised Directive on the re-use of Public Sector Information (PSI) - Commission decision on re-use of its own information Implementation of PSI policy across Europe by ensuring compliance and the development of soft law instruments (e.g. guidelines on licensing and charging) Stakeholder involvement and engagement

10 Open (public) data: Why does it matter for Europe? Ø 1. Untapped business and economic opportunities: data is the new gold; possible direct and indirect gains of 140bln across the EU27; Dutch geo-sector in 2008: jobs Ø 2. Better governance and citizen empowerment: open data increases transparency, citizen participation and administrative efficiency and accountability Ø 3. Addressing societal challenges: data can enhance sustainability of health care systems; essential for tackling environmental challenges Ø 4. Accelerating scientific progress: e-science essential for meeting the challenges of the 21st century in scientific discovery and learning. 10

11 Multilingual (Open) Data infrastructure (2) - Development of European Digital Service Infrastructure and fostering new services and opportunities in relation to - Open Data portals at local, regional and national and European level - "Multilingual access to online services" - Leading by best practice examples

12 the European data portals European Commission data portal (2012) Pan-European open data portal (2013) Multilingual access point to data from across the EU, can be extended to European Open Data Infrastructure through CEF (Connecting Europe Facility) Benefits Scale Interoperability of datasets Easy to find across languages Similar basic use conditions

13 Supporting Research and innovation (3) which fosters the intelligent use, management and reuse of complex and large amount of data for better decision making efficiency knowledge management extraction of embedded intelligence and data insights. including - R&D in Multilingual data and content analytics - Innovation in Data driven intelligence and knowledge management in data intensive sectors - Bubble fish

14 Main Big data research challenges in FP7 (5 calls; >80 projects; >600 contractors; 1500 FTE) Content creation & processing (including multimedia and games) Integration of social software & semantics Personalisation & summarisation Semantic foundations Reasoning (temporal, dimensional and uncertainty, approximate & incomplete reasoning) Knowledge management in business & publicinterest domains Coping with data explosion ("big data" + realtime)

15 Re-launch of funding of LT in 2008 Total funding since then: 160M Research and practical pilot projects Close-to-market solutions for and by SMEs for pooling, sharing and us of language resources New architectures for machine translation Language resources (LR) Automatic mining and processing of LR Multi-lingual/ cross-lingual analytics/ multi-modal analytics Natural spoken interaction Open source provision Standardisation and exchange of best practice

16 Financing and support measures of R&D&I enhancing new data-handling technologies: Ø : ~ 100 million Ø Special cluster: Environmental and geographical data driven projects (econtentplus, CIP and FP7) including support of community building and exchange Ø Related calls of 2013: 48 projects under negotiation FP7 ICT Language technology FP7 SME DCA - Digital Content Analytics FP7 ICT Scalable data analytics CIP 2013: support for technology innovation and uptake (pilot actions, testing, showcasing innovative applications) + priority area envisaged for ICT in Horizon 2020 ( ) 16

17 Outlook: Horizon 2020 Next Research, Development and Innovation programme of EU Excellence in Science - Competitive Industries: leadership in enabling and industrial technologies (LEIT) - Better Society: Societal challenges

18 H2020: Competitive Industries: build leadership in enabling and industrial technologies, with dedicated support for ICT, nanotechnologies, advanced materials, biotechnology, advanced manufacturing and processing, and space, while also providing support for cross-cutting actions to capture the accumulated benefits from combining several Key Enabling Technologies; + facilitate access to risk finance; + provide Union wide support for innovation in SMEs.

19 OUTLOOK CEF: Deployment through digital service infrastructures building block for automated translation Requirements of the digital service infrastructure language resources processing tools and APIs storage space for TMs, LRs, translation models, MT engines processing capacity for MT training, MT operations connection capacity (bandwidth) maintenance, curation, validation, quality control technical helpdesk legal clearance

20 CEF Automated translation building block to focus on enabling other digital service infrastructures: (similar to e-identification, electronic delivery of documents); must be ready for practical results and sufficient quality in "not too distant future"; Should aim at providing translations into the 23 official languages; Shall focus on automated translation paired with language resources, tools, APIs

21 Next steps: Adoption of the legal framework for CEF Put in place institutional set-up for a (provisional) administration of CEF funds Prepare work programme defining the funding priorities Preparation and launch of first calls for tender/ grants Complementary work on securing additional funding in order to complement CEF (national funding, EU structural funds)

22 Funding instrument Public procurement/ calls for tenders Key features (as different from with research funding): Commission specifies exactly what it wants to procure and sets selection and evaluation criteria; Commission details contractual deliverables; Evaluation of tenders by Commission staff; Evaluation on the basis of value for money based on the selection and evaluation criteria; Payment conditional upon acceptance of deliverables by Commission.

23 H2020 events ICT 2013 Vilnius Vilnius, 6-8. November 2013 Thematic, networking and WP sessions Special/thematic Information and networking Luxembourg, January 2014 (tbc)

24 Stakeholder meeting point European Data Forum INFO European Data Forum in Athens Call for contributions

25 Conclusions (1) The business champions of the future will be the most successful companies in coping with multilingual data flood Language technologies offer great opportunities for new business but also lead to some legal issues (e.g. IPR, privacy, copyright, etc) Further development of the Digital Single Market needs new ways and more effort to overcome the language barrier

26 Conclusions (2) Support the better re-use of publicly funded data in Europe The EC is committed to support LT for better multilingual data driven competitiveness and economic opportunities Funds are needed for technology development, but also for language resources. This requires European, national and private sector contributions.

27 Thank you! https://ec.europa.eu/digital-agenda/en/public-sectorinformation-raw-data-new-services-and-products 27

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