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CityPulse: Large-scale data analytics for smart cities

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IEEE iThings2014, Panel Talk (EU-Taiwan collaboration panel), Taipei, Taiwan, 2014.

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CityPulse: Large-scale data analytics for smart cities

  1. 1. CityPulse: Large-scale data analytics for smart cities 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom
  2. 2. Smart City Data − Data is multi-modal and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowed sourced data can be unreliable − Requires (near-) real-time analysis − Privacy and security are important issues − Data alone may not give a clear picture -we need contextual information, background knowledge, multi-source information and obviously better data analytics solutions… 2
  3. 3. Smart City Data 3 ?
  4. 4. What happens if we only focus on data − Number of burgers consumed per day. − Number of cats outside. − Number of people checking their facebook account. − What insight would you draw? 4
  5. 5. What type of problems we expect to solve in “smart” cities
  6. 6. Back to the future 6
  7. 7. Future cities: a view from 1998 Source LAT Times, http://documents.latimes.com/la-2013/ 7
  8. 8. Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/ 8 Source: wikipedia
  9. 9. 9
  10. 10. The IoT and its applications 10 Diffusion of innovation IoT image source: Wikipedia The Most Hyped Technology image source: Forbes via Gartner
  11. 11. Moving fast forward 11 Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/
  12. 12. 12 We need an Integrated Approach
  13. 13. 13 CityPulse Consortium Partners: Industrial SIE (Austria, Romania), ERIC SME AI Higher Education UNIS, NUIG, UASO, WSU City BR, AA Duration: 36 months
  14. 14. 14 Processing steps
  15. 15. CityPulse – what we are going to deliver ... Data Streams a) Software tools/libraries in an integrated framework b) Back-end support servers Smart City Framework Smart City Scenarios a) 101 scenarios b) 10 will be chosen to be prototyped a) Data portals/ real-time access interfaces b) Interoperable formats c) Common interfaces (REST/annotated) a) Proof-of- Concepts and demonstrators and evaluations; Applications/App s/Demos Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  16. 16. Stream Processing ... Data Streams CityPulse
  17. 17. Some of the key issues − Data collection, representation, interoperability − Indexing, search and selection − Storage and provision − Stream analysis, fusion and integration of multi-source, multi-modal and variable-quality data − Aggregation, abstraction, pattern extraction and time/location dependencies − Adaptive learning models for dynamic data − Reasoning methods for uncertain and incomplete data − Privacy, trust, security − Scalability and flexibility of the solutions 17
  18. 18. Some of our recent in this domain 18
  19. 19. Use cases
  20. 20. Scenario ranking
  21. 21. 101 Smart City Use-case Scenarios http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  22. 22. 101 Scenarios − http://www.ict-citypulse.eu/page/content/smart-city- use-cases-and-requirements
  23. 23. Data abstraction 23 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  24. 24. Ontology learning from real world data 24
  25. 25. Adaptable and dynamic learning methods http://kat.ee.surrey.ac.uk/
  26. 26. Social media analysis (collaboration with Kno.e.sis, Wright State University) 26 Tweets from a city City Infrastructure https://osf.io/b4q2t/ P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, under review, 2014.
  27. 27. Correlation analysis 27
  28. 28. 28
  29. 29. Data analytics framework Ambient Intelligence Social systems Interactions Interactions 29 Data Data Data: Domain Knowledge Domain Knowledge Social systems Open Interfaces Open Interfaces Ambient Intelligence Quality and Trust Quality and Trust Privacy and Security Privacy and Security Open Data Open Data
  30. 30. In Conclusion − Smart cities are complex social systems and no technological and data-analytics- driven solution alone can solve the problems. − Combination of data from Physical, Cyber and Social sources can give more complete, complementary data and contributes to better analysis and insights. − Intelligent processing methods should be adaptable and handle dynamic, multi-modal, heterogeneous and noisy and incomplete data. − Effective visualisation and interaction methods are also key to develop successful solutions. − There are several solution for different parts of a data analytics framework in smart cities. An integrated approach is more effective in which IoT devices, communication networks, data analytics and learning algorithms and methods, services and interaction and visualistions and methods (and their optimisation algorithms) can work and cooperate together. 30
  31. 31. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @ictcitypulse p.barnaghi@surrey.ac.uk

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