Urban Computing in LarKC

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 ]

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?”

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) ]

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

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

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

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

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

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

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

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

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/ ]

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

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

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

Urban Computing in LarKC

by Irene Celino

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