The impact of Big Data on next generation of smart cities
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Transcript of The impact of Big Data on next generation of smart cities
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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
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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…
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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/
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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
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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
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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
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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!
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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.
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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.
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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.
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Some of our research in relevant areas
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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.
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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.
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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
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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/
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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 [email protected]