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SC7 Workshop 1: Big Data in Secure Societies

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Soren Auer (IAIS Fraunhofer) presentation for Big Data in Secure Societies Workshop -Brussels, 30th Septmebr 2015-

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SC7 Workshop 1: Big Data in Secure Societies

  1. 1. BIG DATA EUROPE Integrating Big Data, Software & Communities for Addressing Europe’s Societal Challenges
  2. 2. negative conotation Big Data has often a 6-oct.-15www.big-data-europe.eu
  3. 3. Big Data in Marketing 6-oct.-15www.big-data-europe.eu
  4. 4. Big Data in Intelligence 6-oct.-15www.big-data-europe.eu
  5. 5. BigDataEurope aims to help maximizing the societal value of Big Data  Health, demographic change and wellbeing;  Food security, sustainable agriculture and forestry, marine and maritime and inland water research, and the Bioeconomy;  Secure, clean and efficient energy;  Smart, green and integrated transport;  Climate action, environment, resource efficiency and raw materials;  Europe in a changing world - inclusive, innovative and reflective societies;  Secure societies - protecting freedom and security of Europe and its citizens. 6-oct.-15www.big-data-europe.eu
  6. 6. The three Big Data „V“ – Variety is often neglected Quelle: Gesellschaft für Informatik
  7. 7. © Fraunhofer-Allianz Big Data 7 Proactive Maintenance at Rolls Royce New Business Model integrating Sensor Data & Big Data Analytics Dr. Dirk Hecker Condition Monitoring, Proactive maintenance, „Power-by-the-hour“, as-a-service Business Model – payment modell by flight hours Quelle: www.springboeck.ch/SR_Technics.htm © Mark Hillary | Flickr
  8. 8. © Fraunhofer-Allianz Big Data 8 The rolling Smartphone New Business Models for the Automotive Industry with Data Value Chains Dr. Dirk Hecker Windshield wiper as rain sensors for micro wether prognosis Automotive industry can become data provider for other industries Quelle:GTÜ Quelle:www.farming-simulator.com
  9. 9. © Fraunhofer-Allianz Big Data 9 Predictive Analytics Dr. Dirk Hecker From Business Intelligence to Big Data Analytics Business Intelligence Monitoring Predictive Analytics What happened before? What happens now? What will happen soon? What should happen? Prescriptive Analytics „the last Mile“ “prescriptive analytics suggests decision options on how to take advantage of a future opportunity” Quelle: BMW Quelle: www.7-forum.com Quelle: BMW Quelle: Volvo
  10. 10. BigDataEurope Rationale  Show societal value of Big Data  Lower barrrier for using big data technologies o Required effort and resources o Limited data science skills o Lack of Generic Architectures, components  Help establishing cross- lingual/organizational/domain Data Value Chains o Multiple Data Sources o Required: Integration, Harmonisation 6-oct.-15www.big-data-europe.eu
  11. 11. BigDataEurope: Objectives 6-oct.-15www.big-data-europe.eu COORDINATION Stakeholder Engagement (Requirements Elicitation) SUPPORT Design, Realise, Evaluate Big Data Aggregator Platform Create and Manage Societal Big Data Interest Groups Cloud-deployment ready Big Data Aggregator Platform CSA Measures Results
  12. 12. Orthogonal Dimensions of Big Data Ecosystems Generic Big Data Enabling Technologies Data Value Chain Data Generation & Acquisition Data Analysis & Processing Data Storage & Curation Data Visualization & Usage Data-driven Services SocietalChallenges DomainSpecificDataAssets&Technology Healthcare Food Security Energy Intelligent Transport Climate & Environment Inclusive & Reflective Societies Secure Societies
  13. 13. Stakeholder Engagement Cycle
  14. 14. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Open-Source Technologies for Big Data Apps (small selection :-) 14
  15. 15. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Big Data - Technologies Volume VelocityVariety Storm 15
  16. 16. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Groups of Technologies Big Data Technolog ies Data Storage Technologies Data Processin g Workflow Coordinati on Querying/ Processin g Search Data Export/ Import Data Analysis Statistics Text Mining 16
  17. 17. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Big Data Requirements Analysis of historical dta  Millions of entries  Varying analysis quesitions  Years of input data  => Big Data Batch Processing Interactive analysis by online queries  Thousands of users online  Extremely fast response time  Super high availability  => Big Data Databases Analysis of actual data with low latency in "real-time"  React to newest trends  Low-Latency change detection  Real-time online monitoring  => Big Data Stream Processing But how to put it together ? 17
  18. 18. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The „traditional“ Hadoop Ecosystem + NoSQL components a Big Data Management System ZooKeeper askaban Kafka cassandra voldemort MongoDB CouchDB elastic search solr lucene
  19. 19. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Batch Function Speed Function Data Storage pages with postings Batch View Realtime View messagepassing message passing Application Horizontal Scalability in the Lambda Architecture 19 > volume > users > users, volume > velocity> volume, velocity
  20. 20. Blueprint of the Data Aggregator Platform  Follows typical Lambda Architecture  Integrated on top of existing Big Data distribution  + Semantic Layer (Retaining Semantics using LD approach ) Batch Layer Speed Layer Data Storage Real-time data & Transactions … Batch View Real-time View messagepassing message passing Applications & Showcases Real-time dashboards Domain-specific BDE apps Big Data Analytics In-stream Mining BDEPlatform&Intelligence Input data Stream Spatial Social Statistical Temporal Transactional Imagery
  21. 21. BDE Platform based on BigTop Packaging Smoke testing Virtualization Package RPMs and DEBs, so that you can manage and maintain your own cluster. Integrated smoke testing framework Vagrant recipes, raw images, and docker recipes for deploying BigData infrastructures from zero. 6-oct.-15www.big-data-europe.eu + Semantic Layer - Retaining Semantics using Linked Data
  22. 22. Data Aggregator Platform Challenges  Ingest semantic (RDF) and non-semantic (CSV, JSON, XML, …) data o Integrate various mapping techniques (R2RML, CSV on the Web, JSON-LD)  preserve semantics, provenance and metadata in Big Data processing chains o Preserve URI/IRIs o Preserve triples  Exploit semantics for aggregations 6-oct.-15www.big-data-europe.eu
  23. 23. Current Activities – Year#1  2015 BDE Societal Workshops (7) Planned o Schedule on Website  7 W3C Interest Groups set up: Please Join! o SC1: HEALTH https://www.w3.org/community/bde-health/join o SC2: FOOD & AGRICULTURE https://www.w3.org/community/bde-food/ o SC3: ENERGY https://www.w3.org/community/bde-energy/ o SC4: TRANSPORT https://www.w3.org/community/bde-transport/ o SC5: CLIMATE & ENVIRONMENT https://www.w3.org/community/bde-climate/ o SC6: SOCIETIES https://www.w3.org/community/bde-societies/ o SC7: SECURITY https://www.w3.org/community/bde-secure-societies/ www.big-data-europe.eu
  24. 24. BDE Partners
  25. 25. Sören Auer Big Data Europe Coordinator Fraunhofer IAIS & University of Bonn auer@cs.uni-bonn.de Thanks 6-oct.-15www.big-data-europe.eu
  26. 26. Energy/Climate Example: Greenshifting 6-oct.-15www.big-data-europe.eu