Kimmo Rossi. European Commission DG CONNECT

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1 Kimmo Rossi European Commission DG CONNECT Unit G.3 - Data Value Chain SC1 info day, Brussels 5/12/2014 1

2 What we do Unit CNECT.G3 Data Value Chain FP7/CIP/H2020 project portfolio: Big Data, analytics, language technology Contractual Public-Private Partnership (cppp) on Big Data (signed 13 Oct 2015) European Data value chain strategy (see: Communication "Towards a thriving data economy") Public Sector Information (PSI) directive, Open Data Portal 2

3 Definitions What is Big Data? Very difficult to define (precisely) data is "big" if it defies traditional processing & storage paradigms bigness becomes part of the problem the "3Vs": volume (size), velocity (bytes/s), variety (database, jpeg, video, numbers, text in language X...)...to which we add the 4 th V to denote creation of Value (by linking, aggregating, analysing, visualizing...) 4

4 Links to SC1 Big Data and Health? Healthcare is already heavily present in the FP7 Data management project portfolio. Examples: SEMCARE: semantic data platform for healthcare: managing (multilingual) data related to clinical trials and drug safety KHRESMOI: Medical information analysis and retrieval: searching and retrieving useful information from huge amounts of medical imaging data BioBankCloud: storage, analysis and inter-connection of biobank data BioASQ: biomedical semantic indexing and question answering 5

5 Links to SC1 Data Value PPP and Health? The PPP SRIA identifies healthcare as one of the sectors providing: significant impact on society through Big Data technologies and data ecosystem The PPP SRIA mentions Healthcare as as a strong candidate for a sectoral Lighthouse project an area where patient involvement, privacy and ethics are of specific interest an area of many possible application (e.g. comparative effectiveness research, clinical decision support systems, analytics of clinical operations) 6

6 Data Value PPP Political mission of the PPP Improving the innovation capability of European companies and academic institutes involved in data Increasing Europe's market share in the global data market Fostering growth and jobs in Europe through a thriving data-driven economy Enabling the wide-spread introduction of data technologies in traditional sectors Addressing key societal challenges such as health, energy, transport...

7 Data Value PPP Objectives of the PPP Fostering European big data technology leadership in Europe, in particular by building up a data community, strengthening competence and increasing the number of European data companies, including start-ups Enabling research and innovation activities for the future basis of big data value creation in Europe, including activities related to interoperability and standardisation Facilitating the acceleration of data-driven business ecosystems and new business models with a particular focus on SMEs

8 Objectives of the PPP Demonstrating the value of big data for businesses and the public sector and increasing acceptance by citizens Supporting the application of EU data protection legislation and providing for effective mechanisms to ensure its enforcement in the cloud and for big data

9 The innovative pillars of the Data PPP: ispaces i-spaces: cross-organisational and cross-sector interdisciplinary innovation spaces to anchor the research and innovation projects of the cppp Offer secured environments as accelerators for experiments with both private data and open data will act as incubators for new businesses and for the development of skills, competence and best practices

10 The innovative pillars of the Data PPP: Lighthouse projects Lighthouse Projects: large-scale data-driven innovation demonstration projects that will create high level visibility, awareness and impact, focusing on a given sector Suggested sectors: manufacturing, energy, logistics/transport, health, media

11 H2020 Work Programme Our H2020 topics: Big Data & Open data - Innovation ICT15 Big Data Research ICT16 Cracking the language barrier ICT17 Multimodal & natural interaction ICT22a 12

12 ICT 16: In a nutshell Fundamental research in Big Data technologies, addressing analytics (i.e. data mining, machine learning, language understanding, visualization, scalability, responsiveness). User-defined and industry-validated challenges. a) Research and Innovation Actions (RIA) 36 M - Big Data technologies - Benchmarks b) Support Actions (CSA) 1 M - Challenges & competitions in the area of prediction and deep analysis 13

13 a) Research and Innovation actions 1. Big Data Research and Innovation Assessing and improving the quality of Big Data Methods, architectures, data structures for Big Data Better Big Data analytics Big Data Prediction, visualization Multilingual/multimodal/diverse Big Data 2. Benchmarks for Big Data analysis and prediction setting up data resources & infrastructure for benchmarking in domains of industrial relevance activity should become self-sustaining by end of project! 14

14 Requirements for ICT16.a1 proposals Demonstrate the actual availability of: extremely large and realistically complex European data sets (from the beginning of the action) e.g. European Open data portals, public sector data, Copernicus etc. users/testers for human factors testing, and a serious experimentation methodology 15

15 b) Challenges and Prize schemes Support actions to define challenges and prize schemes for verifiable performance in tasks requiring extremely large scale prediction and deep analysis. Compact consortia are required to organise and run well-publicised fast turn-around prediction competitions based on European datasets of a significant size. Proposals in this category are expected to be short in duration and are not required to provide sustainability strategies past the end of the project. 16

16 Next event We will present Big Data Research (ICT16) at the Information and Networking Day in Brussels 16 January ict-16-big-data-networking-day 19

17 Thank you! 20

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