CityPulse: Large-scale data analytics for smart cities

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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 − 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…

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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

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Processing steps

AnalyticsToolbox

Context-awareDecision Support,

Visualisation

Knowledge-based

Stream Processing

Real-TimeMonitoring &

Testing

Accuracy & Trust

Modelling

SemanticIntegration

On Demand Data

Federation

OpenReferenceData Sets

Real-TimeIoT InformationExtraction

IoT StreamProcessing

Federation ofHeterogenousData Streams

Design-Time Run-Time Testing

Exposure APIs

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

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

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Some of our recent in this domain

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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

23F. 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/

Correlation analysis

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Data analytics framework

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Data:

Data

Domain

Knowledge

Socialsystems

InteractionsOpen Interfaces

Ambient

IntelligenceQuality and Trust

Privacy and

Security

Open Data

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.

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Q&A

− Thank you.

− EU FP7 CityPulse Project:

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

@ictcitypulse

p.barnaghi@surrey.ac.uk