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Big Data Analytics in Public Safety and Personal Security: Challenges and Potential

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Big Data and AEGINS offering welfare and protection of the general public through prevention and protection from dangers affecting safety such as crimes, accidents or disasters

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Big Data Analytics in Public Safety and Personal Security: Challenges and Potential

  1. 1. Big Data Analytics in Public Safety and Personal Security: Challenges and Potential Evmorfia Biliri, Panagiotis Kokkinakos, Ariadni Michailitsi, Dimitris Papaspyros, John Tsapelas, Spiros Mouzakitis National Technical University of Athens Sotiris Koussouris, Fenareti Lampathaki Suite5 Ltd London, UK Yury Glickman Fabian Kirstein Fraunhofer FOKUS Berlin, Germany Presenter: Spiros Mouzakitis 23rd ICE conference program, 27-29th June 2017, Madeira
  2. 2. INTRODUCTION 2 Public safety and personal security A multi-domain sector welfare and protection of the general public through prevention and protection from dangers affecting safety such as crimes, accidents or disasters environmental safety safety from criminal acts safety from accidents food safetyhomeland securityemergency response natural disaster management
  3. 3. CHALLENGES 3 • Lack of data discoverability and linking for more accurate risk models, decision making and proactive thinking. • Lack of a common structure and semantic model for data that bear the same information type and come from similar sources. • Lack of data and knowledge sharing mechanisms that in the case of safety issues are crucial in order to timely take action. • Lack of appropriate mechanisms to handle data heterogeneity, quality and provenance aspects. • Inability to adopt novel data-driven business models in PSPS domains.
  4. 4. OPPORTUNITIES 4 • Data and Service Collaboration in PSPS related domains, including public sector, insurance, environment, health, automotive, smart home, etc. • Leveraging / Querying the plethora of data sources (from other domains) that could further enhance and add value in such baseline services, if properly processed and combined. • Innovative cross-domain services in the private sector that cultivate a more caring and danger mitigating client base.
  5. 5. AEGIS FRAMEWORK 5 Create a curated, semantically enhanced, interlinked and cross-lingual repository for public and personal safety-related big and also small data
  6. 6. 6AEGIS DATA VALUE CHAIN Domains Indicative Data Public sector crime, accident, traffic, health, drug usage, alcohol addiction statistics, racial discrimination, natural disasters and damages, biomedical waste management… Private sector (Insurance, Health, etc.) Various types of data, mostly from silo-ed internal data sources Environment weather, pollution, humidity, sea water levels, … Automotive sensor records, performance statistics, car information… Smart Buildings Occupancy, luminance, air quality, temperature, energy footprint, … Individuals Wearable sensors, personal structured information, health data, social activity data Web 2.0 social media, news, trending topics Open data Sensor/IoT data Secondary Data Scientific Data Open, Social Data Historic Data Environment Data Public Safety Data Automotive Data Smart Home Data Activity/health Data Variety Volume Velocity Veracity Value Public Safety and Personal Security (PSPS)
  7. 7. 7APPROACH
  8. 8. 8ARCHITECTURE
  9. 9. 9APPLICATION 1 - AUTOMOTIVE AND ROAD SAFETY DATA Data from the driver’s vehicle e.g. speed, acceleration, gear shifts, brakes, position, fuel consumption, emission) Road information (e.g. road size, speed limits, curves, accident hot-spots), Environmental information (e.g. weather, traffic) Road safety indicator Advanced Driving Assistance Systems (ADAS) - improve driving safety - guide drivers toward more economic efficiency driving style
  10. 10. 10APPLICATION 2 - SMART HOME AND ASSISTED LIVING Smart Home data Wearables data History and personalization assisted living algorithms and apps enhance elderly’s cognition strengthen personal security Improve health and well-being
  11. 11. Anti-fraud detection 11APPLICATION 3 - INSURANCE SECTOR SERVICES Car Data (driving style, speed, times and places of use, fuel consumption, roads used, etc.) Insurance data Portfolio, CRM, Claim External sources Press, Research data, Environmental data, Black box Personalised early warning system for asset protection
  12. 12. 12FUTURE WORK • Implementation of the AEGIS framework in an online platform • Execution of the applications • AEGIS Framework validation
  13. 13. Questions? www.aegis-bigdata.eu @AegisBigdata This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732189 13

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