European Data Forum 2014Athens, March 20th

Towards a Big Data Public Private Partnership

The Big Data PPP- What is it?

The goal of the Big Data Public Private partnership is to increase the amount of productive European economic activities and the number of European jobs that depend on the availability of high quality data assets and the technologies needed to derive value from them.

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European cross-organizational and cross-sector environments

Meeting point for different stakeholders (small, big companies, academia…supply & demand) to discover economic opportunities based on data integration and analysis

Resources to develop working prototypes to test the viability of actual business development

Availablity of data assets (secure environments to enhance data sharing; i.e. not only open data)

Technologies to derive value from them (this could entail bringing analytics close to data)

Why to join? Value Proposition

Technology and data assets development

Means for benchmarking and testingperformance of some core technologies (querying, indexing, feature extraction, predictive analytics, visualization…)

business applications evaluated according to different criteria (ex. usability)

Development of business modelsOptimizing existing industries

New business models along new value chains

Improvement of the skills of data scientists and domain practitioners (enrich educational offering)

Dissemination of best practices showcases to stimulate big data adoption and transfer of solutions across sectors

Analysis of societal impact transfer of data management practices to domains of societal interest (health, environment…)

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How will this be achieved?

The (innovation) data-driven environments…Should support the legitimate ownership, privacy and security claims of corporate data owners (and their customers) pre-condition

Ethical considerations and legal/regulatory requirements

Motivation for data owners: get access to advanced technologies; discover business opportunities thanks to participants interactions

Motivation for researchers, entrepreneurs, small and large companies: ease of experimentation and business opportunities

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Resources to invest, but not unlimited…Initial steps (planning): Data assets and technologies will be prioritized based on their economic potential

Reporting stage: quantitative evidence on increases in performance for core technologies or reduction in costs for business processes

Capitalize on existing environments and initiatives (avoid duplication of resources: incubators, PPPs, EU infrastructures..:); Welcome federation approaches

PPP KPIs

Building upon existing infrastructures and technologies:data incubators

TeraLab (FR): digital services platform that provides both the research community and businesses, with an environment conducive to research and experimentation focused on innovative applications and industrial prototypes in the field of Big Data

Physical resources (including a substantial processing capacity with several teraoctets of RAM), huge databases and various cutting-edge applications and tools (through SAAS/PAAS model)

Facilitating batch or real-time processing and storage of huge amounts of data

Data assets: anonymous, publicly-available information (e.g. OpenStreetMap, Common Crawl), and open data, but also data which has been processed to render it anonymous, provided by professional sources

Access via secure and ultra-secure systems using technology provided by the CASD (Centre for Secure Remote Access).

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• SDIL (DE): Similar approach in the domains of

• Industry 4.0• Energy• Smart Cities• Health

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Building upon existing infrastructures and technologiesFI PPP: FI-WARE/FI-LAB

The Big Data Analysis Support GE offers a solution for both Big Data Batch processing (BD Crunching) and Big Data Streaming; unified set of tools and APIs allowing developers to program the analysis on large amount of data and extract relevant insights in both scenarios using Map&Reduce (ex. Social Networks analysis, real-time recommendations, etc).

TechnologyA true open innovation ecosystem

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FI-WARE Catalogue (http://catalogue.fi-ware.org)

SRIA as tool for planning and prioritizing investments

Data-driven methodological approach:Identification phase

Identification of data assets available from different EU sectors

Analysis of the demand for those data assets (who needs what and for what purpose)

Identification of R&D&i topics needed to make that possible (ensure value extraction)

Prioritization phase

Assessment of economic activities based on those data assets (relevance of applications, potential economic impact)

Market research, EU competitiveness and opportunities vs. other economies (ex. US); weigth of EU economic sectors

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Input gathered through the BIG project, consultations with relevant stakeholders (ex. nessi, SMEs) and a series of sectorial workshops comprising major economic sectors in Europe (Health, Energy, Geospatial/Environment, Public Sector, Manufacturing…)

SRIA: Initial findings

Wide spectrum of data assets demanded: geospatial data (earth observation including weather, digital elevation models, indoor location, geology, address/postcode datasets, cadastre, land use, oceanography, agriculture, transport); social media (Sentiment data, social networks), European web crawl; economy, statistics, admin and financial data (business registries, demographics , geo-economics), machinery and IoT data, mobility (cars, mobile phones…), clinical records/health, genomics/proteomics

Some data assets already show up as very relevant ones:Because of high demand (cross-sectorial opportunities): geospatial/environmental, social media, business/statistics

Because of weigh/importance of sector: machinery (manufacturing)

Different problems to tackle: while in some cases R&I is needed; in some others is more an issue of making data (easily) available (ex. business registries)

Different readiness of data and industries wrt to value extraction in short-to-medium term (geo vs. heath)

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SRIA: Initial findings (cont.)

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Geospatial dataData assetsearth observation including weather, digital elevation models, indoor location, geology, address/postcode datasets, cadastre, land use, oceanography, agriculture, transport.European demandgeospatial (as core business), energy (to represent and predict consumption patterns and the impact of weather), media (personalisation, content adaptation), mobility/transport/logistics (for planning, traffic management, prediction), manufacturing (supply chain management including atmospheric conditions, indoor smart spaces on factory floors), public sector (cadastres, emergency management, city planning, environmental monitoring, smart cities), insurance.Research and Innovation activitiesHarmonisation across different sources (as a precursor of proper standardisation) linked-data enrichment, archiving, data curation, real time analysis; usability, user experience.

Social mediaData assetsSentiment data, social networksEuropean demandMobility/transport (to track customer satisfaction or emerging crises), Energy (to predict consumption patterns), Advertisement/Retail (to predict buying patterns), Health (to gain a patient-level perspective on aliments, prediction of pandemics), Public security (to predict riots or uprisings or panics, large event management), Media (news, real-time, content adaptation…), Insurances (behavioural analysis) Research and Innovation activitiesGraph mining, real-time performance, visualization, predictive analytics, data protection and privacy technologies; usability, user experience.

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SRIA: Initial findings (cont.)

Harmonization across data sources (standardization of formats for data interoperability and integration)

Data analytics Ex. Hadoop++ - going beyond Hadoop to graphs with trillions of edges; the MapReduce parallelism metaphor does not scale well for graph data, stream data mining, parallelisation of inference, dynamic orchestration of services in multi-server and cloud contexts, predictive analytics coupled with proactive decision making

Data protection and privacy technologies Ex. Real time security mechanism to be used for intruder and fraud detection in data streaming scenarios, security policies for the middleware adding items to database engines and especially for non-sql databases, robust and scalable privacy data mining preserving algorithms

Data visualization

Usability

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SRIA: open for contributions

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Beginning April: official launch Public Consultation + SRIA

BIG DATA SRIA Ready (end of May)

Closing web consultation in 1 month aprox (mid May)

EDF

Q&AThank you!

Nuria de LamaRepresentative to the European CommissionResearch & Innovation nuria.delama@atos.net