CityPulse: Large-scale data analytics for smart cities

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Sefki Kolozali, Daniel Puschmann, and Payam BarnaghiInstitute for Communication Systems (ICS)University of SurreyGuildford, 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

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Smart City Data

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?

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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What type of problems we expect to solve

in “smart” cities

Back to the future

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7Source LAT Times, http://documents.latimes.com/la-2013/

Future cities: a view from 1998

8Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/

Source: wikipedia

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The IoT and its applications

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IoT

Diffusion of innovation

image source: Wikipedia

The Most Hyped Technology

image source: Forbes via Gartner

Moving fast forward

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Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/

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We need an Integrated Approach

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CityPulse Consortium

Industrial SIE (Austria,

Romania), ERIC

SME AI

HigherEducation

UNIS, NUIG,UASO, WSU

City BR, AA

Partners:

Duration: 36 months

City of Aarhus, Denmark

City of Brasov, Romania

CityPulse – what we are going to deliver

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Data Streams

Smart City Framework

Smart City Scenarios

a) Software tools/librariesin an integrated frameworkb) Back-end support servers

a)101 scenariosb)10 will be chosen to be prototyped

a) Data portals/ real-time access interfacesb) Interoperable formatsc) Common interfaces (REST/annotated)

a) Proof-of-Concepts and demonstrators and evaluations;Applications/Apps/Demos

Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements

Stream Processing

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Data Streams

CityPulse

Data analytics framework

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Data:Data

Domain

Knowledge

Social

systems

InteractionsOpen Interfaces

Ambient

IntelligenceQuality and

Trust

Privacy and

Security

Open 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

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F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

Ontology learning from real world data

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Adaptable and dynamic learning methods

http://kat.ee.surrey.ac.uk/

Social media analysis (collaboration with Kno.e.sis, Wright State University)

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City Infrastructure

Tweets from a city

P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, under review, 2014.

https://osf.io/b4q2t/

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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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Q&A

− Thank you.

− EU FP7 CityPulse Project:

http://www.ict-citypulse.eu/

@ictcitypulse

{s.kolozali, d.puschmann, p.barnaghi}@surrey.ac.uk