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Large-scale data analytics for smart cities

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The 4th International Workshop on Cyber-Physical Cloud Computing, Osaka, Japan, August 2014.

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Large-scale data analytics for smart cities

  1. 1. Large-scale data analytics for smart cities 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom The Cyber-Physical Cloud Computing Workshop, August 2014, Osaka, Japan
  2. 2. 2 Things, Data, and lots of it image courtesy: Smarter Data - I.03_C by Gwen Vanhee
  3. 3. 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…
  4. 4. Myths About Big Data − Big Data is only about massive data volume − Big Data means Hadoop − Big Data means unstructured data − If we have enough data we can draw conclusions (enough here often means massive amounts) − NoSQL means No SQL − It is about increasing computational power and taking more data and running data mining algorithms. 4 Some of the items are adapted from: Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/
  5. 5. 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? 5
  6. 6. 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… 6
  7. 7. Smart City Data 7 ?
  8. 8. What type of problems we expect to solve in “smart” cities
  9. 9. Back to the future 9
  10. 10. Future cities: a view from 1998 Source LAT Times, http://documents.latimes.com/la-2013/ 10
  11. 11. Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/ 11 Source: wikipedia
  12. 12. 12
  13. 13. 13 We need an Integrated Approach
  14. 14. 14 Processing steps
  15. 15. 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 15
  16. 16. Some of our recent in this domain 16
  17. 17. Data discovery in the IoT 17 Time Location Type Query pre - procesing Query attributes Information Repository (IR) (archived data) Discovery Server # location # type (DS) Gateway Device/Sensor domain Network/Back-end domain Application/user domain | Type ] [ # location |# Time Distributed/scalable
  18. 18. Large-scale data discovery 18 time location type [[##llooccaattiioonn || ##ttyyppee || ttiimmee]] Query formulating Discovery ID Discovery/ DHT Server Data repository (archived data) #location #type #location #type #location #type Gateway Core network Logical Connection Network Connection Data Seyed Amir Hoseinitabatabaei, Payam Barnaghi, Chonggang Wang, Rahim Tafazolli, Lijun Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", 2014.
  19. 19. Data abstraction 19 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  20. 20. Ontology learning from real world data 20
  21. 21. Adaptable and dynamic learning methods http://kat.ee.surrey.ac.uk/
  22. 22. Social media analysis (collaboration with Kno.e.sis) 22 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.
  23. 23. Correlation analysis 23
  24. 24. Equilibrium in transient and non-uniform world A D B C Image source for equilibrium diagram: John D. Hey, The University of York.
  25. 25. Data analytics framework Ambient Intelligence Social systems Interactions Interactions 25 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
  26. 26. 101 Smart City Use-case Scenarios http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  27. 27. 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. 27
  28. 28. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @ictcitypulse p.barnaghi@surrey.ac.uk

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