SC4 Workshop 1: Simon Scerri (Fraunhofer) - What can big data do for transport?
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What can big data do for transport?
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SC4 Workshop 1: Simon Scerri (Fraunhofer) - What can big data do for transport?
- 1. Empowering Communities with Data Technologies – Transport Year #1 Series of Workshops General Overview BIG DATA EUROPE
- 2. The Motivation – Big Data Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. Source:
- 4. Big Data: Dimensions www.big-data-europe.eu Volume Velocity Variety 100010101010101010101010 010100101010101010010100 101010010100101010010100 101001010100101010100101 010001010101010010101010 101010101001010101010101 001010101110001010101010 101010101001010010101010 101001010010101001010010 101001010010100101010010 101010010101000101010101 00101010101 1000101010 1010101010 1010010100 1010101010 1001010010 ……. …………. …… ………..……. . …………… 1 0 1000101010 1010101010 1010010100 1010101010 100101oo11 Veracity!
- 6. Health Climate Energy Transport Food Societies Security Big Data in Europe: Opportunities www.big-data-europe.eu Loremi psumd olors KSDJ OPSC KKSD KAB LKASJL LAWWD S wpweppe pwpisio we 101010 011010 100101 010 Regional Data Repositories #1: Compile, Harmonise, Publish 101010 011010 100101 010 101010 011010 100101 010 #2: Interlink, Centralise Access, Explore 101010100101010101001 011010001010101010010 101010100001011010001 010101010010101010100 100101010100101010101 001011010001010 Data Eleme nt #3: Analyse, Discover, Visualise #4: Mashup, Cross-domain Exploitation Journalists Authorities
- 7. Examples: 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
- 8. Examples: Realtime Analysis (Cross- Domain) 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. Examples: Proactive Maintenance at Rolls Royce New Business Model integrating Sensor Data & Big Data Analytics Condition Monitoring, Proactive maintenance, „Power-by-the-hour“, as-a-service Business Model – payment model by flight hours Quelle: www.springboeck.ch/SR_Technics.htm © Mark Hillary | Flickr Dr. Dirk Hecker
- 10. Big Data in Europe: Obstacles 22-oct.-15www.big-data-europe.eu #1 Big Data “Variety“ problem Multiple Data Sources Required: Integration, Harmonisation #2 Opening-up Data concerns Loss of control, lack of tracking Reservations about large corporations #3 Limited Skills, Training, Technology Lack of Data Scientists Lack of Generic Architectures, components
- 11. Data Value Chain Evolution 22-oct.-15www.big-data-europe.eu Extraction, Curation Quality, Linking, Integration Publication, Visualization, Analysis Extraction, Curation, Quality, Linking, Integration, Publication, Visualization, Analysis Health Transport Security Extraction Curation Quality Linking Integration Publication Visualization Analysis Data Repositories Linked Open Data Cloud Stage 1 Stage 2 Stage 3 Food SocietiesClimate Energy
- 13. Rationale Show societal value of Big Data Lower barrrier for using big data technologies o Required effort and resources o Limited data science skills Help establishing cross- lingual/organizational/domain Data Value Chains 22-oct.-15
- 14. BigDataEurope: Objectives 22-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
- 16. Big Data Ecosystems: Orthogonal Dimensions 22-oct.-15www.big-data-europe.eu 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
- 17. Work Packages & Phases Community Building M1-M12 M13-M24 M25-M36 Enabling Technologies Component Integration Uptake Integrator Deployment Community Assessment WP3 – Big Data Generic Enabling Technologies & Architecture WP5 – Big Data Integrator Instances WP7 – Dissemination & Communication WP2 – Community Building & Requirements WP4 – Big Data Integrator Platform WP6 – Real-life Deployment & User Evaluation
- 19. 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
- 20. Health Climate Energy Transport Food Societies Security Thank You! www.big-data-europe.eu Loremi psumd olors KSDJ OPSC KKSD KAB LKASJL LAWWD S wpweppe pwpisio we 101010 011010 100101 010 Regional Data Repositories #1: Compile, Harmonise, Publish 101010 011010 100101 010 101010 011010 100101 010 #2: Interlink, Centralise Access, Explore 101010100101010101001 011010001010101010010 101010100001011010001 010101010010101010100 100101010100101010101 001011010001010 Data Eleme nt #3: Analyse, Discover, Visualise #4: Mashup, Cross-domain Exploitation Journalists Authorities
Notes de l'éditeur
- Quelle 4. Bild: http://images.zeit.de/mobilitaet/2014-05/auto-autonom/auto-autonom-540x304.jpg Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.[8]
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- Two clearly defined coordination and support measures: Coordination: Engaging with a diverse range of stakeholder groups from SCs Collecting requirements for the ICT infrastructure needed by data-intensive science practitioners covering all aspects of publishing and consuming semantically interoperable, large-scale data and knowledge assets Support: meets requirements minimises disruption to current workflows, maximises the opportunities to take advantage of the latest European RTD developments BigDataEurope will implement and apply two main instruments to successfully realize these measures: Build Societal Big Data Interest/Community Groups in the W3C interest group scheme & involving a large number of stakeholders from the Horizon 2020 societal challenges as well as technical Big Data experts; Design, integrate and deploy a cloud-deployment-ready Big Data aggregator platform comprising key open-source Big Data technologies for real-time and batch processing, such as Hadoop, Cassandra and Storm.