large-scale data analytics for smart cities

Large-scale data analytics for smart cities

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Payam Barnaghi

Institute for Communication Systems (ICS)

University of Surrey

Guildford, United Kingdom

The Cyber-Physical Cloud Computing Workshop, August 2014, Osaka, JapanThe Cyber-Physical Cloud Computing Workshop, August 2014, Osaka, Japan

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Things, Data, and lots of it

image courtesy: Smarter Data - I.03_C by Gwen Vanhee

Current focus on Big Data

− Emphasis on power of data and data mining solutions

− Technology solutions to handle large volumes of data; e.g. Hadoop, NoSQL, Graph Databases, …

− Trying to find patterns and trends from large volumes of data…

Myths About Big Data

− Big Data is only about massive data volume− Big Data means Hadoop− Big Data means unstructured data− If we have enough data we can draw conclusions

(enough here often means massive amounts)− NoSQL means No SQL− It is about increasing computational power and

taking more data and running data mining algorithms.

4Some of the items are adapted from: Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/

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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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 type of problems we expect to solve in

“smart” cities

Back to the future

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10Source LAT Times, http://documents.latimes.com/la-2013/

Future cities: a view from 1998

11Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/

Source: wikipedia

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We need an Integrated Approach

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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

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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Data discovery in the IoT

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Time

Location

Type

Qu

ery

pre

-p

roce

ssin

g

Query attributes Information

Repository (IR)(archived data)

# location# type

Discovery Server (DS)

Gateway

Device/Sensor domain

Network/Back-enddomain

Application/userdomain

[ # lo

catio

n |#

Tim

e | T

ype

]

Distributed/scalable

Large-scale data discovery

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timetime

locationlocation

typetype

Query formulatingQuery formulating

[#location | #type | time][#location | #type | time]

Discovery IDDiscovery ID

Discovery/DHT ServerDiscovery/DHT Server

Data repository(archived data)Data repository(archived data)

#location#type

#location#type

#location#type

GatewayGateway

Core networkCore network

Network Connection

Logical Connection

Data

Seyed Amir Hoseinitabatabaei, Payam Barnaghi, Chonggang Wang, Rahim Tafazolli, Lijun Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", 2014.

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)

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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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AD

CB

Image source for equilibrium diagram: John D. Hey, The University of York.

Equilibrium in transient and non-uniform world

Data analytics framework

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Data:

DataData

Domain

KnowledgeDomain

Knowledge

Social

systemsSocial

systems

InteractionsInteractionsOpen

InterfacesOpen

Interfaces

Ambient

IntelligenceAmbient

IntelligenceQuality and

TrustQuality and

Trust

Privacy and

SecurityPrivacy and

Security

Open DataOpen Data

101 Smart City Use-case Scenarios

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

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