An overview of the AEGIS Project presented by Dr. Yury Glikman (Fraunhofer FOKUS) during the "Information and Networking Days on Horizon 2020 Big Data Public-Private Partnership topics 2017" on January 17th, 2017 in Luxembourg.

1. AEGIS
Advanced Big Data Value Chains for Public Safety
and Personal Security
Dr. Yury Glikman
AEGIS Project Manager
Fraunhofer FOKUS
17 January 2017 Information and Networking Day ICT 14, 15, 16, 17 - Luxembourg

2. Key Facts
 Topic: ICT-14-2016 - Big Data PPP: cross-sectorial and cross-lingual data integration
and experimentation
 Type of Action: Innovation Action
 Project start: 1 January 2017
 Duration: 30 months
 Project Coordinator: Fraunhofer FOKUS (Germany)
 Consortium: 10 organizations from 8 EU member states
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3. Consortium
 FRAUNHOFER FOKUS – Germany (coordinator)
 GFT Italia Srl – Italy
 KTH – Sweden
 VIF Kompetenzzentrum – Austria
 UBITECH – Cyprus
 National Technical University of Athens – Greece
 École Polytechnique Fédérale de Lausanne – Switzerland
 SUITE5 Ltd – UK
 HYPERTECH – Greece
 Hdi Assicurazioni S.P.A. – Italy
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4. Public Safety and Personal Security (PSPS)
 PSPS - Welfare and protection of the general public and of individuals through
prevention and protection from dangers affecting safety such as crimes, accidents or
disasters
 Obvious interest in public and personal safety - both from the public and the private
sector
 Today PSPS services are based on fragmented and domain-specific data
 Huge potential for combining data from different domains for PSPS services
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5. Challenges
 Lack of a common structure and semantic model
 Data discoverability
 Data Quality
 Lack of data and knowledge sharing mechanisms
 Lack of business models
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6. AEGIS Concept
17 January 2017 Information and Networking Day ICT 14, 15, 16, 17 - Luxembourg
AEGIS will bring together data owners related to PSPS issues
and enable their interrelation and interoperation
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7. Four-DimensionalAEGIS Offering
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8. Architecture
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9. Scientific and Innovation Objectives
 To identify and semantically link diverse cross-sector and multi-lingual information
sources contributing to the generation of a trustful data sharing value chain around
Pubic Safety
 To supply a scientifically rigorous methodology for data handling micro-services that
compose a novel Big Data Architecture allowing constant expansion
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10. Technical Objectives
 To roll-out improved intelligence conveying cross-sector and multi-lingual tools,
turning the Big Data 4Vs (Volume, Variety, Veracity, Velocity) into Value
 To deliver an open, secure, privacy-respectful, configurable, scalable cloud based Big
Data infrastructure as a Service benefiting all actors in the value chain
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11. Business Objectives
 To validate and optimise the AEGIS platform through a set of long-lasting
demonstrators in three (3) different sectors
 To introduce new Business Models and Data-driven Shared Economy principles in
different business sectors through the offering of AEGIS as-a-Service
 To establish an Open Innovation Community supporting real-life demonstration
applications and expanding the usage of the AEGIS platform to all stakeholders
affected
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12. Demonstrator 1 – Integrated Road Safety Indicator (by VIF )
17 January 2017 Information and Networking Day ICT 14, 15, 16, 17 - Luxembourg
 Combination of vehicle lifecycle datasets and datasets from other domains:
 Traffic, weather, maps, accidents, crimes, media, air quality, etc
 The indicator will be used by
on-board Advanced Driving
Assistance Systems (ADAS) to
improve driving safety and
guide drivers toward more
economic efficiency driving
style
 AEGIS will help to create a
more mature driver safety
model
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13. Demonstrator 2 – Smart Home andAssisted Living
(by HYPERTECH + SUITE5 + UBITECH)
 Personalised guidance towards elderly people
 Smart Home Automation for Security and Well-being enhancement
 Data:
 broadcast and social media data, crime data, geo-location, weather,
wearable sensors, health provider records, and announcements and
personal health vulnerability data
 sensing data (temperature, humidity, luminance, CO2, VOC, occupancy)
 Target customers: service providers in the area of social/ health-care
and assisted living
 enabling proactive interventions, fine-grained and personalised service
offerings, along with enhancement of service effectiveness through
significant improvements in elderly’s quality of life
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14. Demonstrator 3 – Insurance Sector: Personalised Early Warning System for
Asset Protection (by Hdi + Gft)
 Personalised intelligence for preventing specific catastrophic incidents related to
people lives/assets through early warning notifications
 Data:
 Insurance incidents data, insurance data, location data, media, social media data, crime
data, other statistics, weather, traffic, etc.
 Reduced number of insurance cases and costs
The usage statisctics of the demostrators and underlying data assest will recorded,
evaluated and reported.
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15. www.aegis-bigdata.eu
Thank you!
Yury Glikman
yury.glikman@fokus.fraunhofer.de
17 January 2017 Information and Networking Day ICT 14, 15, 16, 17 - Luxembourg