Post on 13-Jul-2020
SDA-SmartCity
Overview and Challenges
Alessandra Mileo
Senior Research Fellow
Insight Centre for Data Analytics, NUI Galway
alessandra.mileo@insight-centre-org
What do you expect from this Tutorial?
• What can Semantics do for Smart Cities
• What semantics cannot do for Smart Cities
• What should Data Analytics for Smart Cities do
• What open problems do you see/foresee?
SDA$SmartCity,@,ESWC15,,31st,May,2015,,Portoroz,Slovenia, 2,
From Lab to Reality
3,
Lab,Data/Experiments, Real,Data/Experiments,
IoT and IoE
4,
5
Things, Data, and lots of it
image,courtesy:,Smarter,Data,$,I.03_C,by,Gwen,Vanhee,
Scale of the problem
6
Things, Data,
Devices,
2.5,quinSllion,bytes,per,day,,
Billions,and,Billions,of,them…,
EsSmated,50,Billion,by,2020,
Scale of the problem
7
Things, Data,
Devices,
2.5,quinSllion,bytes,per,day,,
Billions,and,Billions,of,them…,
EsSmated,50,Billion,by,2020,
People,
3,billions,,growing,by,1,billion,every,5,years,
hXp://www.internetlivestats.com/internet$users/,
Myths About Big Data
• Big Data is only about massive data volume • Big Data means Hadoop • Big Data means unstructured data • If we have enough data we can draw conclusions (enough
here often means massive amounts) • NoSQL means No SQL • It is all about increasing computational power and taking
more data and running data mining algorithms.
8 Some of the items are adapted from: Brain Gentile, http://mashable.com/2012/06/19/big-data-myths/
Beyond conventional sensors
• Human as a sensor (citizen sensors) – e.g. tweeting real world data and/or events
• Software sensors – e.g. Software agents/services generating/representing
data
9
Road block, A3
Road block, A3 Suggest a different route
Not just Volume… … but also Data Dynamicity:
How,can,we,efficiently,deal,with:,$ Large&amounts&of&(heterogeneous/distributed)&data?&$ Both&sta7c&and&dynamic&data?&$ In&a&re;usable,&modular,&flexible&way?&$ Integrate&different&types&of&data&$ Provide&hypothesis&and&create&more&context;aware&solu7ons&
Adapted from: M. Hauswirth. A. Mileo, Insight, National University of Ireland, Galway.
AnyPlace) AnyTime)
AnyThing)
Data)Volume)
Security,)Reliability,))Trust))and)Privacy)
Societal)Impacts,)Economic)Values))and)Viability))
Services)and)Applica?ons)
Networking)and)Communica?on)
Data Analytics for Smart Cities
• Great opportunities and many applications;
• Enhanced and (near-) real-time insights;
• Supporting more automated decision making and in-depth analysis of
events and occurrences by combining various sources of data;
• Providing more and better information to citizens;
• …
12
However…
• We need to know our data and its context (density, quality, reliability, …)
• Open Data (there needs to be more real-time data)
• Complementary data
• Citizens in control
• Transparency and data management issues (privacy, security, trust, …)
• Reliability and dependability of the systems
13
Data,is,not,what,we,want,or,is,it?,
From Lab to Reality
15,
Lab,Data/Experiments, Real,Data/Experiments,
Problem #1
• Data: We seem to have lots of it…
• Real World Data: it is always difficult to get (silos, format, privacy, business interests or lack of interest!...)
16,
Problem #2
• Data: interoperability and metadata frameworks…
• Real World Data: there are solutions for service based (RESTful) access, meta-data/semantic representation frameworks (e.g. W3C SSN, HyperCat,…) but none of them are still widely adopted.
17,
How many of you had to create/extend ‘yet another ontology’?
Problem #3
• Data: quality, reliability…
• Real World Data: data can be noisy, crowed source data can be inaccurate, contradictory, delay in accessing/processing the data…
18,
Problem #4
• Data: having too much data and using analytics tools alone won’t solve the problem, cause the more the data, the more the mess
• Real World Data: in addition to the HPC issues, we need new methods/solutions that can provide real-time analysis of dynamic, variable quality and multi-modal streams…
19,
Problem #5
• Data: abstraction, discovering the associations…
• Real World Data: co-occurrence vs. causation; we need hypothesis, background knowledge,…
• After all data is not what we are really after…
20,
What,we,need,are,mechanisms,to,collect,,represent,and,USE,the,data,to,find,insights,and,transform,them,
into,acSonable$knowledge,,
What happens if we only focus on data
• Number of burgers consumed per day.
• Number of cats outside.
• Number of people checking their facebook account.
22
What insight would you draw?
What,type,of,problems,we,expect,to,solve,in,“smart”,ciSes,,
24
Image&courtesy:&LA&Times,&hHp://documents.la7mes.com/la;2013/&
Future Cities: A view from 1998
25
Image&courtesy:,hXp://robertluisrabello.com/denial/traffic$in$la/#gallery[default]/0/,Source: wikipedia
Back to the Future: 2013
26
Back to the Future: Galway, 2014
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Back to the Future: Galway, 2015
Lowering Bridge 2015
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Back to the Future: Galway, 2015
Traffic issue, the plan
Another example
• 100 Millions to upgrade the IT system
• 80 Millions left after staff cost
• That would only buy a class B system!
29
= Problem not solved
Data is the Difference
• Same problems over and over again
• What’s the difference now?
• We have DATA! (*)
30,
(*) Handle with care!
Smart City Data Analysis
• Analysis of thousands of traffic, pollution, weather, congestion, public transport, waste and event sensory data to provide better transport and city management.
• Converting smart meter readings to information that can help prediction and balance of power consumption in a city.
• Monitoring elderly homes, personal and public healthcare applications.
• Event and incident analysis and prediction using (near) real-time data collected by citizen and device sensors.
• Turning social media data (e.g. Tweets) related to city issues into event and sentiment analysis.
• Any many more…
31
The benefits of data processing in IoT
• 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 and smart city management.
• Monitor patients, elderly care and much more…
• Requires: real-time, reliable, efficient (for low power and resource limited nodes), and scalable solutions.
32 Partially adapted from: What is Bog Data?, IBM
Smart City Data
• Data is multi-modal and heterogeneous
• Noisy and incomplete
• Time and location dependent
• Dynamic and varies in quality
• Crowed sourced data can be unreliable
• Requires (near-) real-time analysis
• Privacy and security are important issues
• Data can be biased- we need to know our data!
• Data alone may not give a clear picture -we need contextual information, background knowledge, multi-source information and obviously better data analytics solutions…
33
Smart Data Collection
• Smart Data Collection
• Intelligent Data Processing
(selective attention and information-extraction)
• Region Beta Paradox: people can sometimes recover more quickly from more intense emotions or pain than from less distressing experiences
34
image&source:&KRISTEN&NICOLE,&siliconangle.com&&
35
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.
36
Storing, handling and processing the data
Image courtesy: IEEE Spectrum,,
• British Library, West Yorkshire, Northern Englad
• 150 years of newspaper
How do we find data in Smart Cities?
We need an Integrated Approach
37
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AnalyticsToolbox
Context0awareDecision4Support,
Visualisation
Knowledge0basedStream4
Processing
Real0TimeMonitoring4&
Testing
Accuracy4&4Trust
Modelling
SemanticIntegration
On4Demand4Data
Federation
OpenReferenceData4Sets
Real0TimeIoT4InformationExtraction
IoT4StreamProcessing
Federation4ofHeterogenousData4Streams
Design0Time Run0Time Testing
Exposure4APIs
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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 (Semantic interoperability) • 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.
Designing for City Problems
101 Smart City Use-case Scenarios
41 hXp://www.ict$citypulse.eu/scenarios/,
Use-case Scenarios
42,hXp://www.ict$citypulse.eu/scenarios/,
Data Lifecycle
43
Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
Big (IoT) Data Analytics
.,
.,
.,
Real World (Live) Data
Smart,City,Framework,
Smart,City,Scenarios,
Dynamic Data/Event Visualisation
45,
Reference Datasets
46
hXp://iot.ee.surrey.ac.uk:8080/datasets.html,
Importance of Complementary Data
47
Wrap-Up Session
SDA$SmartCity,@,ESWC15,,31st,May,2015,,Portoroz,Slovenia,
48,
What will have seen
49,
• Challenges in using Semantics in Smart Cities
• Virtualization and Validation of Smart City Data
• Quality-aware Stream Discovery, query processing and event
filtering
• Rule-based and hybrid complex reasoning (potentials)
• Data summarization and aggregation
• Usecase Demonstrators from EU FP7 CityPulse
Back to our questions
50,
• What can Semantics can do for Smart Cities?
• What Semantics cannot do for Smart Cities
• What should Data Analytics for Smart Cities do?
• What open problems do you see/foresee?