Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

The impact of Big Data on next generation of smart cities

714 Aufrufe

Veröffentlicht am

Veröffentlicht in: Technologie
  • Login to see the comments

  • Gehören Sie zu den Ersten, denen das gefällt!

The impact of Big Data on next generation of smart cities

  1. 1. 1 The impact of Big Data on next generation of smart cities Payam Barnaghi Driving Innovation and Corporate Entrepreneurship (DICE) 6th February 2014 University of Surrey
  2. 2. 2 Big Data? What is it?
  3. 3. 3 Image courtesy: the Economist
  4. 4. 4 Image courtesy: http://www.informationweek.com
  5. 5. Current focus on Big Data − Emphasis on power of data and data mining solutions − Technology solutions to handle large volumes of data; e.g. Hadoop, NoSQL, Graph Databases, … − Trying to find patterns and trends from large volumes of data…
  6. 6. Top 5 Myths About Big Data − Big Data is only about massive data volume − Big Data means Hadoop − Big Data means unstructured data − Big Data is for social media feeds and sentiment analysis − NoSQL means No SQL 6 Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/
  7. 7. What happens if we only focus on data − Number of burgers consumed per day. − Number of cats outside. − Amount of rain fall. − What insight would you draw? 7
  8. 8. … but also Data Dynamicity: Not just Volume… How can we efficiently deal with: - Large amounts of (heterogeneous/distributed) data? - Both static and dynamic data? - In a re-usable, modular, flexible way? - Integrate different types of data - Provide hypothesis and create more context-aware solutions Adapted from: M. Hauswirth. A. Mileo, Insight, National University of Ireland, Galway.
  9. 9. 9 What are the key trends?
  10. 10. 10
  11. 11. 11 "intelligence is becoming ambient" Satya Nadella, Microsoft CEO
  12. 12. Connected world 12Image courtesy: Wilgengebroed DataData SemanticsSemantics Social networks Social networks
  13. 13. 13
  14. 14. 14 Image courtesy: IEEE Computer Society
  15. 15. 15 Smart Cities and Back to the future
  16. 16. 16Source LAT Times, http://documents.latimes.com/la-2013/ Future cities: a view from 1998
  17. 17. 17 Image courtesy: Avatar wiki
  18. 18. 18
  19. 19. 19
  20. 20. Big Data for Smart Cities −Big data should help: −empower citizens −provide more business opportunities for companies (and SMEs) and private sector services −create better governance of our cities and better public services −provide smarter monitoring and control −improve energy efficiency, create greener environments… −create better healthcare, elderly-care…
  21. 21. 21 Sensor devices are becoming widely available - Programmable devices - Off-the-shelf gadgets/tools
  22. 22. 22 More “Things” are being connected Home/daily-life devices Business and Public infrastructure Health-care …
  23. 23. 23
  24. 24. 24 People Connecting to Things Motion sensor Motion sensor Motion sensor ECG sensor World Wide Web Road block, A3 Road block, A3
  25. 25. 25 Cyber, Physical and Social Data
  26. 26. 26 Citizen Sensors Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).
  27. 27. 27 Data in smart cities − Turn 12 terabytes of Tweets created each day into sentiment analysis related to different events/occurrences or relate them to products and services. − Convert (billions of) smart meter readings to better predict and balance power consumption. − Analyze thousands of traffic, pollution, weather, congestion, public transport and event sensory data to provide better traffic management. − Monitor patients, elderly care and much more… Adapted from: What is Bog Data?, IBM
  28. 28. 28 Cities are Complex Social Systems
  29. 29. 29 and Data alone won’t solve all the problems
  30. 30. 30 “Raw data is both an oxymoron and bad data” Geoff Bowker, 2005 Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.
  31. 31. 31 Do we need all this data?
  32. 32. 32 Perceptions and Intelligence Data Information Knowledge Wisdom Raw sensory data Structured data (with semantics) Abstraction and perceptions Actionable intelligence
  33. 33. Current portals 33
  34. 34. “People want answers, not numbers” (Steven Glaser, UC Berkley) Sink node Gateway Core network e.g. Internet What is the temperature at home?Freezing!
  35. 35. Big Data is not we need, what we need is Smart Data*. * Amit Sheth, “Transforming Big Data into Smart Data”, Kno.e.sis, Wright State University, 2013.
  36. 36. Smart Data − Data with the right semantics, annotations − Provenance, quality of information − Interpretable formats − Links and interconnections − Background knowledge, domain information − Hypotheses, expert knowledge − Adaptable and context-aware solutions 36
  37. 37. Smart Data is the starting point to create an efficient set of Actions. The goal is to create actionable knowledge.
  38. 38. 38 Data alone is not enough − Domain knowledge − Machine interpretable meta-data − Delivery, sharing and representation services − Query, discovery, aggregation services − Publish, subscribe, notification, and access interfaces/services − More open solutions for innovation and citizen participation − Efficient feedback and control mechanisms − Social network and social system analysis − In cities, interactions with people and social systems is the key.
  39. 39. 39 Storing, handling and processing the data Image courtesy: IEEE Spectrum
  40. 40. 40 Technical Challenges − Discovery: finding appropriate device and data sources − Access: Availability and (open) access to data resources and data − Search: querying for data − Integration: dealing with heterogeneous devices, networks and data − Large-scale data mining, adaptable learning and efficient computing and processing − Interpretation: translating data to knowledge that can be used by people and applications − Scalability: dealing with large numbers of devices and a myriad of data and the computational complexity of interpreting the data.
  41. 41. Social Challenges − Transforming traditional perceptions of physical objects, online engagement and social interactions. − Implications of the confluence of physical-cyber- social systems on societies, including aspects such as citizen participation, democracy, open government, open government data and others. − How to solve the real problems… 41 A. Sheth, P. Barnaghi, M. Strohmaier, R. Jain, S.Staab (editors), Physical-Cyber-Social Computing (Dagstuhl Reports 13402), Dagstuhl Reports, vol. 3, no.9, pp. 245-263, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, January, 2014.
  42. 42. Some of our research in relevant areas
  43. 43. Large-scale data discovery 43
  44. 44. Learning from real world data 44 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  45. 45. Stream Processing 45 http://kat.ee.surrey.ac.uk/ F. Ganz, P. Barnaghi, F. Carrez, "Multi-resolution data communication in wireless sensor networks," IEEE IoT World Forum, 2014.
  46. 46. CityPulse 46
  47. 47. 47
  48. 48. In summary 48 Data: DataData Domain Knowledge Domain Knowledge Social systems Social systems InteractionsInteractions Open Interfaces Open Interfaces Ambient Intelligence Ambient IntelligenceQuality and Trust Quality and Trust Privacy and Security Privacy and Security Open DataOpen Data
  49. 49. 49 Challenges and opportunities − Providing infrastructure − Publishing, sharing, and accessing solutions on both local and global scales − Indexing and discovery (data and resources) − Aggregation, integration and fusion − Trust, privacy, ownership and security − Data mining and creating actionable knowledge − Integration into services and applications in e-health, the public sector, retail, manufacturing and personalized apps. − Mobile apps, location-based services, monitoring control etc. − Social aspects: cities are complex social systems − New business models
  50. 50. 50 Image courtesy: http://www.theatlanticcities.com/
  51. 51. Acknowledgments − Prof. Amit Sheth (Kno.e.sis, Wright State University), Frieder Ganz (UniS), Dr. Amir HosseiniTabatabie (Unis), Pramod Anantharam (Kno.e.sis). 51
  52. 52. 52 Thank you.
  53. 53. 53 Payam Barnaghi Centre for Communication Systems Research Faculty of Engineering and Physical Sciences University of Surrey p.barnaghi@surrey.ac.uk