Invited talk, Large scale data analytics for smart cities and related use cases, The 5th EU-Japan Symposium on ICT Research and Innovation, October 2014, European Commission, Brussels, Belgium.

1. Large scale data analytics for smart
cities and related use cases
1
Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom
The 5th EU-Japan Symposium on ICT Research and Innovation
16-17 October 2014, Brussels, Belgium

2. 2
“[The] past is gone and irrecoverable, and wise men have
enough to do with things present and to come.”
Francis Bacon (1561-1626)

3. 3
Things and Data: Past-Current-Future
image courtesy: Smarter Data - I.03_C by Gwen Vanhee

4. 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.

5. 5
CityPulse: Large-scale data analytics
for smart cities

6. What type of problems we expect to solve
in
“smart” cities

7. Future cities: A view from 1998
Source LAT Times, http://documents.latimes.com/la-2013/ 7

8. 8
Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia

9. 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…

10. Designing for City Problems

11. 101 Smart City Use-case Scenarios
http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements

12. 12
101 Smart City Use-case Scenarios
Image source: Alexandra Institute, Denmark, CityPulse Project.

13. Big (IoT) Data Analytics
...
Real World Data
Smart City Framework
Smart City Scenarios

14. Data Processing and Information Extraction
Analysis of traffic data in City of Aarhus
University of Surrey Smart Campus data analysis
Twitter data analysis for detecting city events

15. Large-scale data discovery
15
time
location
type
[[##llooccaattiioonn || ##ttyyppee || ttiimmee]]
Query formulating
Discovery ID
Discovery/
DHT Server
Data repository
(archived data)
#location
#type
#location
#type
#location
#type
Gateway
Core network
Logical Connection
Network Connection
Data
Probability
Source: Amir Hoseini Tabatabaie, et al, University of Surrey/InterDigital.

16. Data analytics
Ambient
Intelligence
Social
systems Interactions Interactions
16
Data Data
Data:
Domain
Knowledge
Domain
Knowledge
Social
systems
Open
Interfaces
Open
Interfaces
Ambient
Intelligence
Quality and
Trust
Quality and
Trust
Privacy and
Security
Privacy and
Security
Open Data Open Data

17. 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.
17

18. − Thank you.
− EU FP7 CityPulse Project:
http://www.ict-citypulse.eu/
@pbarnaghi
p.barnaghi@surrey.ac.uk