1
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
2
Big Data?
What is it?
3
Image courtesy: the Economist
4
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?
7
… 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.
9
What are the key trends?
10
11
"intelligence is becoming ambient"
Satya Nadella, Microsoft CEO
Connected world
12Image courtesy: Wilgengebroed
DataData
SemanticsSemantics
Social
networksSocial
networks
13
14
Image courtesy: IEEE Computer Society
15
Smart Cities and Back to the future
16Source LAT Times, http://documents.latimes.com/la-2013/
Future cities: a view from 1998
17
Image courtesy: Avatar wiki
18
19
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…
21
Sensor devices are becoming widely available
- Programmable devices- Off-the-shelf gadgets/tools
22
More “Things” are being connected
Home/daily-life devicesBusiness and Public infrastructureHealth-care…
23
24
People Connecting to Things
Motion sensorMotion sensor
Motion sensor
ECG sensor
World Wide Web
Road block, A3Road block, A3Road block, A3Road block, A3
25
Cyber, Physical and Social Data
26
Citizen Sensors
Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).
27
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
28
Cities are Complex Social Systems
29
and
Data alone won’t solve all the problems
30
“Raw data is both an oxymoron and bad data”
Geoff Bowker, 2005
Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.
31
Do we need all this data?
32
Perceptions and Intelligence
Data
Information
Knowledge
Wisdom
Raw sensory data
Structured data (with semantics)
Abstraction and perceptions
Actionable intelligence
Current portals
33
“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
36
Smart Data is the starting point to create an efficient
set of Actions.
The goal is to create actionable knowledge.
38
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.
39
Storing, handling and processing the data
Image courtesy: IEEE Spectrum
40
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
43
Learning from real world data
44
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
Stream Processing
45
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
46
47
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
48
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
49
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
50
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).
51
52
Thank you.
53
Payam BarnaghiCentre for Communication Systems ResearchFaculty of Engineering and Physical SciencesUniversity of [email protected]
Top Related