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!

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= 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…

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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&&

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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,,

•  British Library, West Yorkshire, Northern Englad

•  150 years of newspaper

How do we find data in Smart Cities?

We need an Integrated Approach

37

38

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?