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Physical-Cyber-Social Data Analytics & Smart City Applications

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5th Annual International Cyber-Physical Cloud Computing Workshop, Washington DC, USA.

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Physical-Cyber-Social Data Analytics & Smart City Applications

  1. 1. Physical-Cyber-Social Data Analytics & Smart City Applications 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom 5th Annual International Cyber-Physical Cloud Computing Workshop
  2. 2. Cyber-Physical-Social Data 2P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology (IET), I. Borthwick (editor), March 2015.
  3. 3. Internet of Things: The story so far RFID based solutions Wireless Sensor and Actuator networks , solutions for communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, early concepts and demos, … Motion sensor Motion sensor ECG sensor Physical-Cyber-Social Systems, Linked-data, semantics, More products, more heterogeneity, solutions for control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and Industry and B2B services/applications, more Standards… P. Barnaghi, A. Sheth, "Internet of Things: the story so far", IEEE IoT Newsletter, September 2014. 3
  4. 4. 4 “Each single data item is important.” “Relying merely on data from sources that are unevenly distributed, without considering background information or social context, can lead to imbalanced interpretations and decisions.” ?
  5. 5. Data- Challenges − 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 can be biased- we need to know our data! 5
  6. 6. Data Lifecycle 6 Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  7. 7. Device/Data interoperability 7 The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  8. 8. W3C semantic sensor network ontology (SSNO) http://www.w3.org/2005/Incubator/ssn/ssnx/ssn M. Compton, P. Barnaghi, L. Bermudez, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012. 8
  9. 9. Semantic annotation and vocabularies P. Barnaghi, M. Presser, K. Moessner, "Publishing Linked Sensor Data", in Proc. of the 3rd Int. Workshop on Semantic Sensor Networks (SSN), ISWC2010, 2010. 9
  10. 10. IoTLite Ontology 10http://iot.ee.surrey.ac.uk/fiware/ontologies/iot-lite
  11. 11. Search on the Internet/Web in the early days 1111
  12. 12. A discovery engine for the IoT 12A. HosseiniTabatabaie, P. Barnaghi, C. Wang, L. Dong, R. Tafazolli, "Method and Apparatus for Scalable Data Discovery in IoT Systems”, US Patents, CNV12174, May 2014. Let’s assume that attribute x has an alphabet Ax ={ax1,…,axs}. Query for a data item (q) that is described with attributes x, y and z, is then represented as q={x=axk & y=ayl & z=azm} The average ratio of matching processes that are required to resolve this query at n:
  13. 13. IoT environments are usually dynamic and (near-) real-time 13 Off-line Data analytics Data analytics in dynamic environments Image sources: ABC Australia and 2dolphins.com
  14. 14. Creating Patterns- Adaptive sensor SAX 14 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.
  15. 15. From SAX patterns to events/occurrences 15 F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.
  16. 16. From patterns/events to a situation ontology 16 F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.
  17. 17. Learning ontology from sensory data 17
  18. 18. KAT- Knowledge Acquisition Toolkit http://kat.ee.surrey.ac.uk/ F. Ganz, D. Puschmann, P. Barnaghi, F. Carrez, "A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things", IEEE Internet of Things Journal, 2015. 18
  19. 19. Real world data 19
  20. 20. Analysing social streams 20 With
  21. 21. City event extraction from social streams 21 Tweets from a city POS Tagging Hybrid NER+ Event term extraction GeohashingGeohashing Temporal Estimation Temporal Estimation Impact Assessment Impact Assessment Event Aggregation Event AggregationOSM LocationsOSM Locations SCRIBE ontologySCRIBE ontology 511.org hierarchy511.org hierarchy City Event ExtractionCity Event Annotation P. Anantharam, P. Barnaghi, K. Thirunarayan, A.P. Sheth, "Extracting City Traffic Events from Social Streams", ACM Trans. on Intelligent Systems and Technology, 2015. Collaboration with Kno.e.sis, Wright State University
  22. 22. CRF formalisation – for annotation 22 A General CRF Model
  23. 23. Geohashing 23 0.6 miles Max-lat Min-lat Min-long Max-long 0.38 miles 37.7545166015625, -122.40966796875 37.7490234375, -122.40966796875 37.7545166015625, -122.420654296875 37.7490234375, -122.420654296875 4 37.74933, -122.4106711 Hierarchical spatial structure of geohash for representing locations with variable precision. Here the location string is 5H34 0 1 2 3 4 5 6 7 8 9 B C D E F G H I J K L 0 1 7 2 3 4 5 6 8 9 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8
  24. 24. Automated creation of training data 24 Evaluation over 500 randomly chosen tweets from around 8,000 annotated tweets
  25. 25. Extracted events and the ground truth 25Open source software: https://osf.io/b4q2t/
  26. 26. Social media analysis 26 http://iot.ee.surrey.ac.uk/citypulse-social/
  27. 27. Not so good examples 27
  28. 28. CityPulse demo 28
  29. 29. CityPulse: live events from the city of Aarhus 29 http://www.ict-citypulse.eu/ 29
  30. 30. Deep IoT 30
  31. 31. In summary 31 “The ultimate goal is transforming the raw data to insights and actionable knowledge and/or creating effective representation forms for machines and also human users and creating automation.” This usually requires data from multiple sources, (near-) real time analytics and visualisation and/or semantic representations.
  32. 32. 32 “Data will come from various source and from different platforms and various systems.” This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services). Semantic interoperability is also a key requirement.
  33. 33. IoT discovery engines? 33 “Working across different systems and various platforms is a key requirement. Internet search engines work very well with textual data, but IoT data comes in various forms and often as streams.” This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services).
  34. 34. IoT discovery engines? 34 “ To make it more complex, IoT resources are often mobile and/or transient. Quality and trust (and obviously privacy) are among the other key challenges”. This requires efficient distributed index and update mechanisms, quality-aware an resource- aware selection and ranking, and privacy control and preservation methods (and governance models) .
  35. 35. Accessing IoT data 35 “ The internet/web norm (for now) is usually searching for the data; the search engines are usually information locators – return the link to the information; IoT data access is more opportunistic and context aware”. This requires context-aware and opportunistic push mechanism, dynamic device/resource associations and (software-defined) data routing networks.
  36. 36. Web search is already adapting this model 36 Image credits: the Economist
  37. 37. The future: borders will blend 37Source: IEEE Internet Computing, Special issue on Physical-Cyber-Social Computing
  38. 38. In conclusion − IoT data analytics is different from common big data analytics. − Data collection in the IoT comes at the cost of bandwidth, network, energy and other resources. − Data collection, delivery and processing is also depended on multiple layers of the network. − We need more resource-aware data analytics methods and cross-layer optimisations (Deep IoT). − The solutions should work across different systems and multiple platforms (Ecosystem of systems). − Data sources are more than physical (sensory) observation. − The IoT requires integration and processing of physical-cyber-social data. − The extracted insights and information should be converted to a feedback and/or actionable information. 38
  39. 39. Smart city datasets 39 http://iot.ee.surrey.ac.uk:8080
  40. 40. IET sector briefing report 40 Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  41. 41. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @pbarnaghi p.barnaghi@surrey.ac.uk

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