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Urban Computing in LarKC



Joint Work with Emanuele Della Valle ...

Joint Work with Emanuele Della Valle
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