Urban Computing in LarKC http:// www.larkc.eu Irene Celino Project Manager – Senior Consultant – “Semantic Web & Web 3.0” Practice Coordinator CEFRIEL – ICT Institute – Politecnico di Milano

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

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

Some 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?”

“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?”

Urban Computing as an Answer to Them

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

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

Dimension of Urban Computing

Urban Computing Use Case in LarKC

Data 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

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

Are Mashups the Solution? [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 / ]

Mashups offer powerful visualization tools 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 /

… and simple programming abstractions

Not Everything Boils Down to Plumbing

Requirements 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

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

Coping 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

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

Coping with multiple reasoning paradigms precise and vs. approximate consistent inference reasoning [ source http://senseable.mit.edu/ ]

Open World vs. Closed World Assumption Assumption [source: http://gizmodo.com/photogallery/trafficsky/1003143552 ] Supporting Heterogeneity 1/2

Open World vs. Closed World

Assumption Assumption

Unique Name Assumption in multiple models representing reality at different granularities Supporting Heterogeneity 2/2 1 2 29 30 L3 L3

Unique Name Assumption in multiple models

representing reality at different granularities

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 Data

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

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.

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 and inconsistent data

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 Data Scale The advent of Pervasive Computing and Web 2.0 technologies led to a constant ly growing amount of interconnected data about urban environments [source: http://senseable.mit.edu/nyte/ ]

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

Usage 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

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

Alpha Urban LarKC High Level Architecture LarKC platform Interface Urban Computing Environment SPARQL query SPARQL result REST request JSON response Request data Data Pipelines Config. PROBLEM : Which Milano monuments or events or friends can I quickly get to from here? Streets Monuments Events Data & Index

Alpha 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

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

Much More to Come! Keep an eye on http://wiki.larkc.eu/UrbanComputing

Thanks for your attention! Irene Celino email: [email_address] web: http://www.cefriel.it , http://swa.cefriel.it Semantic Web Practice CEFRIEL – ICT Institute – Politecnico di Milano