BIG - NESSI Networking Session, Talk by Nuria de Lama, Representative to the European Commission, Research & Innovation ATOS, Spain at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Towards a Big Data Public Private Partnership

1. European Data Forum 2014
Athens, March 20th

2. 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)

3. Why to join? Value Proposition
Technology and data assets development
Means for benchmarking and testing
performance of some core technologies (querying, indexing, feature
extraction, predictive analytics, visualization…)
business applications evaluated according to different criteria (ex. usability)
Development of business models
Optimizing 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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4. 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

5. 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

6. Building upon existing infrastructures and technologies
FI 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).
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Technology
A true open innovation
ecosystem

7. FI-WARE Catalogue (http://catalogue.fi-ware.org)
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8. 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…)

9. 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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10. SRIA: Initial findings (cont.)
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Geospatial data
Data assets
earth observation including weather, digital
elevation models, indoor location, geology,
address/postcode datasets, cadastre, land
use, oceanography, agriculture, transport.
European demand
geospatial (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 activities
Harmonisation across different sources (as a
precursor of proper standardisation) linked-
data enrichment, archiving, data curation,
real time analysis; usability, user experience.
Social media
Data assets
Sentiment data, social networks
European demand
Mobility/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 activities
Graph mining, real-time performance, visualization,
predictive analytics, data protection and privacy
technologies; usability, user experience.
… …

11. 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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12. 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

13. Q&A
Thank you!
Nuria de Lama
Representative to the European
Commission
Research & Innovation
nuria.delama@atos.net