1. 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. 2
Big Data?
What is it?

3. 3
Image courtesy: the Economist

4. 4
Image courtesy: http://www.informationweek.com

5. 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…

6. 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
6
Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/

7. 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

8. … 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. 9
What are the key trends?

10. 10

11. 11
"intelligence is becoming ambient"
Satya Nadella, Microsoft CEO

12. Connected world
12Image courtesy: Wilgengebroed
DataData
SemanticsSemantics
Social
networks
Social
networks

13. 13

14. 14
Image courtesy: IEEE Computer Society

15. 15
Smart Cities and
Back to the future

16. 16Source LAT Times, http://documents.latimes.com/la-2013/
Future cities: a view from 1998

17. 17
Image courtesy: Avatar wiki

18. 18

19. 19

20. 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. 21
Sensor devices are becoming widely available
- Programmable devices
- Off-the-shelf gadgets/tools

22. 22
More “Things” are being connected
Home/daily-life devices
Business and Public
infrastructure
Health-care
…

23. 23

24. 24
People Connecting to Things
Motion sensor
Motion sensor
Motion sensor
ECG sensor
World Wide Web
Road block, A3
Road block, A3

25. 25
Cyber, Physical and Social Data

26. 26
Citizen Sensors
Source: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).

27. 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. 28
Cities are Complex Social Systems

29. 29
and
Data alone won’t solve all the
problems

30. 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. 31
Do we need all this data?

32. 32
Perceptions and Intelligence
Data
Information
Knowledge
Wisdom
Raw sensory data
Structured data (with
semantics)
Abstraction and perceptions
Actionable intelligence

33. Current portals
33

34. “People want answers, not numbers”
(Steven Glaser, UC Berkley)
Sink
node Gateway
Core network
e.g. Internet
What is the temperature at home?Freezing!

35. 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.

36. 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

37. Smart Data is the starting point to create an
efficient set of Actions.
The goal is to create actionable knowledge.

38. 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. 39
Storing, handling and processing
the data
Image courtesy: IEEE Spectrum

40. 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.

41. 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…
41
A. 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.

42. Some of our research in relevant areas

43. Large-scale data discovery
43

44. Learning from real world data
44
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

45. 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.

46. CityPulse
46

47. 47

48. In summary
48
Data:
DataData
Domain
Knowledge
Domain
Knowledge
Social
systems
Social
systems
InteractionsInteractions
Open
Interfaces
Open
Interfaces
Ambient
Intelligence
Ambient
IntelligenceQuality and
Trust
Quality and
Trust
Privacy and
Security
Privacy and
Security
Open DataOpen Data

49. 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. 50
Image courtesy: http://www.theatlanticcities.com/

51. Acknowledgments
− Prof. Amit Sheth (Kno.e.sis, Wright State University),
Frieder Ganz (UniS), Dr. Amir HosseiniTabatabie (Unis),
Pramod Anantharam (Kno.e.sis).
51

52. 52
Thank you.

53. 53
Payam Barnaghi
Centre for Communication Systems Research
Faculty of Engineering and Physical Sciences
University of Surrey
p.barnaghi@surrey.ac.uk