Large scale data analytics for smart cities and related use cases

Large scale data analytics for smart cities and related use cases

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Payam Barnaghi

Institute for Communication Systems (ICS)

University of Surrey

Guildford, United Kingdom

The 5th EU-Japan Symposium on ICT Research and Innovation16-17 October 2014, Brussels, Belgium

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“[The] past is gone and irrecoverable, and wise men have enough to do with things present and to come.”

Francis Bacon (1561-1626)

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Things and Data: Past-Current-Future

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, co-occurrences and trends from large volumes of data…

− The IoT is a dynamic environment and involves lots of heterogeneous data and (often resource constraints) devices; so the data analytics for the IoT have different requirements.

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CityPulse: Large-scale data analytics for smart cities

What type of problems we expect to solve in

“smart” cities

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

Future cities: A view from 1998

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

Source: wikipedia

Big Data for Smart Cities

−Big data should help:−empower citizens−provide more business opportunities for companies

(and SMEs) and private sector services−create better governance of our cities and better

public services −provide smarter monitoring and control− improve energy efficiency, create greener

environments… −create better healthcare, elderly-care…

Designing for City Problems

101 Smart City Use-case Scenarios

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

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101 Smart City Use-case Scenarios

Image source: Alexandra Institute, Denmark, CityPulse Project.

Big (IoT) Data Analytics

.

.

.

Real World Data

Smart City Framework

Smart City Scenarios

Data Processing and Information Extraction

Analysis of traffic data in City of AarhusAnalysis of traffic data in City of Aarhus

University of Surrey Smart Campus data analysisUniversity of Surrey Smart Campus data analysis

Twitter data analysis for detecting city eventsTwitter data analysis for detecting city events

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

Prob

abili

ty

Source: Amir Hoseini Tabatabaie, et al, University of Surrey/InterDigital.

Data analytics

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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

Looking back, looking forward

− Combining 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 able to handle dynamic, multi-modal, heterogeneous and noisy and incomplete data.

− Future work on resource-aware data analytics, data discovery protocols and techniques, data combination, integration and mash-up and real world use-case implementations.

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− Thank you.

− EU FP7 CityPulse Project:

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

@pbarnaghi

[email protected]