Urban Computing in LarKC ITN – Infrastructure, Telematics and Navigation – Turin, October 15 th-16th 2009 - © CEFRIEL 2009

Urban Computing in LarKC

http://www.larkc.eu

Irene CelinoProject Manager – Senior Consultant – “Semantic Web & Web 3.0” Practice Coordinator

CEFRIEL – ICT Institute – Politecnico di Milano

Turin, October 16th 2009 – © CEFRIEL 20092Urban Computing in LarKC

Cities Are AliveCities Are Alive

Cities born, grow, evolve like living beings.

The state of a city changes continuously, influenced by a lot of factors,

human ones: people moving in the city or extending it

natural ones: precipitations or climate changes

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[source http://www.citysense.com]

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Some Mobile Users’ QuestionSome Mobile Users’ Question

“Is public transportation where I am?” “Is the event where I am the one that attract more people

right now?” “Where are all my friends meeting?” “Is the traffic moving where I’m going?”

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Urban Computing as an Answer to ThemUrban Computing as an Answer to Them

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[source IEEE Pervasive Computing,July-September 2007 (Vol. 6, No. 3)]

Urban ComputingUrban Computing

The integration of computing, sensing, and actuation technologies into everyday urban settings and lifestyles.

Urban settings include, for example, streets, squares, pubs, shops, buses, and cafés - any space in the semipublic realms of our towns and cities.

Only in the last few years have researchers paid much attention to technologies in these spaces.

Pervasive computing has largely been applied either in relatively homogeneous rural areas, where researchers have

added sensors in places such as forests, vineyards, and glaciers or, on the other hand, in small-scale, well-defined patches of the built

environment such as smart houses or rooms. Urban settings are challenging for experimentation and deployment,

and they remain little explored

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Dimension of Urban ComputingDimension of Urban Computing

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Urban Computing Use Case in LarKCUrban Computing Use Case in LarKC

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Data AvailabilityData Availability Some years ago, due to the lack of data, Urban Computing looked

like a Sci-Fi idea. Nowadays, a large amount of the required information can be made

available on the Internet at almost no cost. We are running a survey [1,2] and we have collected more than 50 sources of data:

maps (Google, Yahoo!, Wikimapia, OpenStreetMap), events scheduled (Eventful, Upcoming…), voluntarily provided users location (Google Latitude), mass presence and movements multimedia data with information about location (Flickr…) relevant places (schools, bus stops, airports...) traffic information (accidents, problems of public transportation...) city life (job ads, pollution, health care...)

[1] http://wiki.larkc.eu/UrbanComputing/ShowUsABetterWay [2] http://wiki.larkc.eu/UrbanComputing/OtherDataSources

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Are Mashups the Solution?Are Mashups the Solution?

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[source: http://www-01.ibm.com/software/lotus/products/mashups/ ]

IBM Lotus Mashups

[source: http://editor.googlemashups.com ]

[source: http://pipes.yahoo.com/pipes/ ]

[source: http://www.popfly.com/ ]

[source: http://openkapow.com/ ]

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Mashups offer powerful visualization toolsMashups offer powerful visualization tools

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Google Charts API

http://code.google.com/apis/chart/ http://maps.google.it/

http://maps.yahoo.com/

MIT Simile Timeline & Timeplot

http://simile.mit.edu/timeline/ http://simile.mit.edu/timeplot/

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… … and simple programming abstractionsand simple programming abstractions

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Not Everything Boils Down to PlumbingNot Everything Boils Down to Plumbing

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Requirements for Mobile Data MashupsRequirements for Mobile Data Mashups

Urban Computing encompass sensing, actuation and computing requirements.

Many previous work in the area of Pervasive and Ubiquitous Computing investigated requirements in sensing, actuation, and several aspects of computation (from hardware to software, from networks to devices)

In LarKC we focus on Knowledge Representation and Reasoning requirements

Hereafter we exemplify the need to cope with representational, reasoning, and defaults heterogeneity scale time-dependency noisy, uncertain and inconsistent data

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Coping with representational heterogeneityCoping with representational heterogeneity It is an obvious requirement

data always come in different formats (syntactic and structural heterogeneity)

the problem of merging and aligning data is a structural problem of system interoperability

while the perfect “one-size-fit-all” solution does not exist, a comprehensive array of partial solutions exit

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Coping with multiple reasoning paradigms Coping with multiple reasoning paradigms

precise and vs. approximate

consistent inference reasoning

[ source http://senseable.mit.edu/ ]

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Open World vs. Closed World

Assumption Assumption

[source: http://gizmodo.com/photogallery/trafficsky/1003143552 ]

Supporting Heterogeneity 1/2Supporting Heterogeneity 1/2

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Unique Name Assumption in multiple models

representing reality at different granularities

1 2

29

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L3L3

Supporting Heterogeneity 2/2Supporting Heterogeneity 2/2

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Nature of changing data Periodically changing data

Pure periodic law Probabilistic law

Event driven changing data

Mean time between changes Slow Medium Fast

Coping with Changing DataCoping with Changing Data

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Coping with Changing KnowledgeCoping with Changing Knowledge Invariant knowledge

it includes obvious terminological knowledge

such as an address is made up by a street name, a civic number, a city name and a ZIP code

less obvious nomological knowledge that describes how the world is expected

to be e.g., given traffic lights are switched off or

certain streets are closed during the night to evolve

e.g., traffic jams appears more often when it rains or when important sport events take place

Invariant data do not change in the observation

period, e.g. the names and lengths of the roads.

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©2009 Google – Imagery @2009 Teleatlas – Terms of Usage

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Traffic data are a very good example of such data.

Different sensors observing the same road may give apparently inconsistent information.

Moreover, a single datum coming from a sensor a given moment may have multiple possible meanings.

Coping with noisy, uncertain Coping with noisy, uncertain and inconsistent dataand inconsistent data

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Coping with Data ScaleCoping with Data Scale

The advent of Pervasive Computing and Web 2.0 technologies led to a constantly growing amount of interconnected data about urban environments

[source: http://senseable.mit.edu/nyte/]

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Usage Scenario of Alpha Urban LarKCUsage Scenario of Alpha Urban LarKC

A user is in a (potentially unknown) city and would like to organize a day/night of visiting some places, meeting friends, attending a musical concert, etc.

He needs to: Find interesting destinations:

Monuments or relevant places in the city Events that take places in the city

Understand the most suitable way to reach them

To solve the problem today, the user would have to: use multiple applications, and manually pass intermediate results from a service to another

one

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Alpha Urban LarKC High Level ArchitectureAlpha Urban LarKC High Level Architecture

LarKC platform

Interface

Urban Computing Environment

SPARQL

query

SPARQL

result

REST

request

JSONresponse

Request data Data

PipelinesConfig.

PROBLEM: Which Milano monuments or events or friends can I quickly get to from here?

StreetsMonumentsEventsData & Index

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Alpha Urban LarKC demoAlpha Urban LarKC demo

Demo publicly available at: http://seip.cefriel.it/alpha-Urban-LarKC/ Explanatory video at: http://seip.cefriel.it/alpha-Urban-LarKC/alpha-

Urban-LarKC-demo.htm

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Much More to Come! Much More to Come!

Keep an eye on http://wiki.larkc.eu/UrbanComputing

Urban Computing in LarKC ITN – Infrastructure, Telematics and Navigation – Turin, October 15 th-16th 2009 - © CEFRIEL 2009

Thanks for your attention!

Irene Celino

email: irene.celino@cefriel.itweb: http://www.cefriel.it, http://swa.cefriel.it

Semantic Web PracticeCEFRIEL – ICT Institute – Politecnico di Milano