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The impact of Big Data on next generation of smart cities

Payam Barnaghi

Driving Innovation and Corporate Entrepreneurship (DICE)6th February 2014

University of Surrey

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Big Data?

What is it?

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Image courtesy: the Economist

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Image courtesy: http://www.informationweek.com

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…

Top 5 Myths About Big Data

− Big Data is only about massive data volume− Big Data means Hadoop− Big Data means unstructured data− Big Data is for social media feeds and sentiment

analysis− NoSQL means No SQL

6Brain 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.− Amount of rain fall.

− What insight would you draw?

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… but also Data Dynamicity:

Not just Volume…

How can we efficiently deal with:- Large amounts of (heterogeneous/distributed) data?- Both static and dynamic data?- In a re-usable, modular, flexible way?- Integrate different types of data- Provide hypothesis and create more context-aware solutions

Adapted from: M. Hauswirth. A. Mileo, Insight, National University of Ireland, Galway.

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What are the key trends?

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"intelligence is becoming ambient"

Satya Nadella, Microsoft CEO

Connected world

12Image courtesy: Wilgengebroed

DataData

SemanticsSemantics

Social

networksSocial

networks

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Image courtesy: IEEE Computer Society

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Smart Cities and Back to the future

16Source LAT Times, http://documents.latimes.com/la-2013/

Future cities: a view from 1998

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Image courtesy: Avatar wiki

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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…

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Sensor devices are becoming widely available

- Programmable devices- Off-the-shelf gadgets/tools

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More “Things” are being connected

Home/daily-life devicesBusiness and Public infrastructureHealth-care…

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People Connecting to Things

Motion sensorMotion sensor

Motion sensor

ECG sensor

World Wide Web

Road block, A3Road block, A3Road block, A3Road block, A3

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Cyber, Physical and Social Data

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Citizen Sensors

Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).

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Data in smart cities

− Turn 12 terabytes of Tweets created each day into sentiment analysis related to different events/occurrences or relate them to products and services.

− Convert (billions of) smart meter readings to better predict and balance power consumption.

− Analyze thousands of traffic, pollution, weather, congestion, public transport and event sensory data to provide better traffic management.

− Monitor patients, elderly care and much more…

Adapted from: What is Bog Data?, IBM

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Cities are Complex Social Systems

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and

Data alone won’t solve all the problems

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“Raw data is both an oxymoron and bad data”

Geoff Bowker, 2005

Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.

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Do we need all this data?

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Perceptions and Intelligence

Data

Information

Knowledge

Wisdom

Raw sensory data

Structured data (with semantics)

Abstraction and perceptions

Actionable intelligence

Current portals

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“People want answers, not numbers” (Steven Glaser, UC Berkley)

Sinknode Gateway

Core networke.g. Internet

Core networke.g. Internet

What is the temperature at home?What is the temperature at home?Freezing!Freezing!

Big Data is not we need, what we need is

Smart Data*.

* Amit Sheth, “Transforming Big Data into Smart Data”, Kno.e.sis, Wright State University, 2013.* Amit Sheth, “Transforming Big Data into Smart Data”, Kno.e.sis, Wright State University, 2013.

Smart Data

− Data with the right semantics, annotations− Provenance, quality of information− Interpretable formats− Links and interconnections− Background knowledge, domain information− Hypotheses, expert knowledge − Adaptable and context-aware solutions

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Smart Data is the starting point to create an efficient

set of Actions.

The goal is to create actionable knowledge.

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Data alone is not enough

− Domain knowledge− Machine interpretable meta-data− Delivery, sharing and representation services− Query, discovery, aggregation services− Publish, subscribe, notification, and access

interfaces/services− More open solutions for innovation and citizen participation− Efficient feedback and control mechanisms − Social network and social system analysis− In cities, interactions with people and social systems is the

key.

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Storing, handling and processing the data

Image courtesy: IEEE Spectrum

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Technical Challenges

− Discovery: finding appropriate device and data sources− Access: Availability and (open) access to data resources and

data− Search: querying for data− Integration: dealing with heterogeneous devices, networks

and data− Large-scale data mining, adaptable learning and efficient

computing and processing − Interpretation: translating data to knowledge that can be

used by people and applications− Scalability: dealing with large numbers of devices and a

myriad of data and the computational complexity of interpreting the data.

Social Challenges

− Transforming traditional perceptions of physical objects, online engagement and social interactions.

− Implications of the confluence of physical-cyber-social systems on societies, including aspects such as citizen participation, democracy, open government, open government data and others.

− How to solve the real problems…

41A. Sheth, P. Barnaghi, M. Strohmaier, R. Jain, S.Staab (editors), Physical-Cyber-Social Computing (Dagstuhl Reports 13402), Dagstuhl Reports, vol. 3, no.9, pp. 245-263, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, January, 2014.

Some of our research in relevant areas

Large-scale data discovery

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Learning from real world data

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

Stream Processing

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

F. Ganz, P. Barnaghi, F. Carrez, "Multi-resolution data communication in wireless sensor networks," IEEE IoT World Forum, 2014.F. Ganz, P. Barnaghi, F. Carrez, "Multi-resolution data communication in wireless sensor networks," IEEE IoT World Forum, 2014.

CityPulse

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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

In summary

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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

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Challenges and opportunities

− Providing infrastructure − Publishing, sharing, and accessing solutions on both local and global

scales− Indexing and discovery (data and resources)− Aggregation, integration and fusion− Trust, privacy, ownership and security− Data mining and creating actionable knowledge

− Integration into services and applications in e-health, the public sector, retail, manufacturing and personalized apps.− Mobile apps, location-based services, monitoring control etc.

− Social aspects: cities are complex social systems− New business models

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Image courtesy: http://www.theatlanticcities.com/

Acknowledgments

− Prof. Amit Sheth (Kno.e.sis, Wright State University), Frieder Ganz (UniS), Dr. Amir HosseiniTabatabie (Unis), Pramod Anantharam (Kno.e.sis).

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Thank you.

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Payam BarnaghiCentre for Communication Systems ResearchFaculty of Engineering and Physical SciencesUniversity of Surreyp.barnaghi@surrey.ac.uk