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Urban Computing in LarKC

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Joint Work with Emanuele Della Valle ...

Joint Work with Emanuele Della Valle
emanuele.dellavalle@polimi.it.
Presentation of the results of the Urban Computing use case of the LarKC project. Speech at the ITN Expo event (http://www.itnexpo.it/) on October 16th, 2009.

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    Urban Computing in LarKC Urban Computing in LarKC Presentation Transcript

    • 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