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CityPulse: Large-scale data analytics 
for smart cities 
1 
Sefki Kolozali, Daniel Puschmann, and Payam Barnaghi 
Institute for Communication Systems (ICS) 
University of Surrey 
Guildford, United Kingdom
Smart City Data 
− Data is multi-modal and heterogeneous 
− Noisy and incomplete 
− Time and location dependent 
− Dynamic and varies in quality 
− Crowd sourced data can be unreliable 
− Requires (near-) real-time analysis 
− Privacy and security are important issues 
− Data alone may not give a clear picture 
2
Smart City Data 
3 
?
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
What type of problems we expect to solve 
in “smart” cities
Back to the future 
6
Future cities: a view from 1998 
7 Source LAT Times, http://documents.latimes.com/la-2013/
Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/ 8 
Source: wikipedia
9
The IoT and its applications 
10 
Diffusion of innovation 
IoT 
image source: Wikipedia 
The Most Hyped Technology 
image source: Forbes via Gartner
Moving fast forward 
11 
Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/
12 
We need an Integrated Approach
13 
CityPulse Consortium 
Partners: 
Industrial 
SIE (Austria, 
Romania), 
ERIC 
SME AI 
Higher 
Education 
UNIS, NUIG, 
UASO, WSU 
City BR, AA 
Duration: 36 months
14 
City of Aarhus, Denmark
15 
City of Brasov, Romania
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/Apps/D 
emos 
Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
Stream Processing 
... 
Data Streams 
CityPulse
Data analytics framework 
29 
Data:
Use cases
Scenario ranking
101 Smart City Use-case Scenarios 
http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
101 Scenarios 
− http://www.ict-citypulse.eu/page/content/smart-city- 
use-cases-and-requirements
Data abstraction 
23 
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
Ontology learning from real world data 
24
Adaptable and dynamic learning 
methods 
http://kat.ee.surrey.ac.uk/
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.
28
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. 
30
Q&A 
− Thank you. 
− EU FP7 CityPulse Project: 
http://www.ict-citypulse.eu/ 
@ictcitypulse 
{s.kolozali, d.puschmann, 
p.barnaghi}@surrey.ac.uk

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IoTMeetupGuildford#4: CityPulse Project Overview - Sefki Kolozali, Daniel Puschmann, Payam Barnaghi (University of Surrey)

  • 1. CityPulse: Large-scale data analytics for smart cities 1 Sefki Kolozali, Daniel Puschmann, and Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom
  • 2. Smart City Data − Data is multi-modal and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowd sourced data can be unreliable − Requires (near-) real-time analysis − Privacy and security are important issues − Data alone may not give a clear picture 2
  • 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. What type of problems we expect to solve in “smart” cities
  • 6. Back to the future 6
  • 7. Future cities: a view from 1998 7 Source LAT Times, http://documents.latimes.com/la-2013/
  • 9. 9
  • 10. The IoT and its applications 10 Diffusion of innovation IoT image source: Wikipedia The Most Hyped Technology image source: Forbes via Gartner
  • 11. Moving fast forward 11 Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/
  • 12. 12 We need an Integrated Approach
  • 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 City of Aarhus, Denmark
  • 15. 15 City of Brasov, Romania
  • 16. 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/Apps/D emos Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  • 17. Stream Processing ... Data Streams CityPulse
  • 21. 101 Smart City Use-case Scenarios http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  • 22. 101 Scenarios − http://www.ict-citypulse.eu/page/content/smart-city- use-cases-and-requirements
  • 23. Data abstraction 23 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  • 24. Ontology learning from real world data 24
  • 25. Adaptable and dynamic learning methods http://kat.ee.surrey.ac.uk/
  • 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. 28
  • 28. 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. 30
  • 29. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @ictcitypulse {s.kolozali, d.puschmann, p.barnaghi}@surrey.ac.uk