The document discusses the CityPulse project, which uses large-scale data analytics to help smart cities. It notes that smart city data is multi-modal, heterogeneous, noisy and incomplete. CityPulse will develop an integrated framework and tools to intelligently process this complex data from physical, cyber and social sources to generate insights and solutions for smart city problems. It will test scenarios in the cities of Aarhus and Brasov and demonstrate applications to address issues like traffic and infrastructure management using real-time data streams.
IoTMeetupGuildford#4: CityPulse Project Overview - Sefki Kolozali, Daniel Puschmann, Payam Barnaghi (University of Surrey)
1. CityPulse: Large-scale data analytics
for smart cities
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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
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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?
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5. What type of problems we expect to solve
in “smart” cities
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CityPulse Consortium
Partners:
Industrial
SIE (Austria,
Romania),
ERIC
SME AI
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
Duration: 36 months
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
23. Data abstraction
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F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
26. Social media analysis (collaboration with Kno.e.sis,
Wright State University)
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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.
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