Introduction to Urban Computing in LarKC

Introduction to Urban Computing in LarKC – Cefriel, 28th June 2011

Urban Computing in LarKC

Irene Celino, Daniele Dell’Aglio, Emanuele Della Valle

CEFRIEL – ICT Institute, Politecnico di Milano

LarKC – the vision

Web-scale Data

Cefriel, 28th June 20112Introduction to Urban Computing in LarKC

1 triple:

Distance Sun – Pluto ~1014 triples (estimate on

no. triples on the Web)

See also: http://www.youtube.com/watch?v=hjUbbl4cnAE

LarKC – the vision

Web-scale Reasoning


prec

isio

n (s

ound

ness

)

recall (completeness)

Logics

IR

Semantic Web

� Giving up 100% correctness:� trading quality for size� often completeness is

not needed� sometimes even

correctness is not needed

� � not only logics!

See also: http://www.youtube.com/watch?v=hjUbbl4cnAE

The LarKC project

� LarKC – the Large Knowledge Collider – is a platform for massive distributed incomplete reasoning to remove the scalability barriers of existing reasoning systems for the Semantic Web

� Technology pillars:� logic-based reasoning � information retrieval� machine learning� databases� probabilistic reasoning� cognitively-inspired approaches � high-performance computing

Factsheet:� 10M€ budget� 3.5 years (ending Sept 2011)� >80 person years� 16 partners, 3 use case � obtained in FP7 Call1

Outcomes:� LarKC Platform on

Sourceforge (now v 2.5) with Apache license

� >25 plug-ins for the platform� Several demonstrators� >90 publications


http://www.larkc.eu/

http://www.youtube.com/watch?v=hjUbbl4cnAE

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

Urban Computing


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

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

� Only in the last few years research paid attention to urban environments

� Pervasive computing is more and more applied � Great success of Location-based Services and Location-based Social

Networks with increasing amounts of data

� � Urban settings are challenging for experimentation & deployment

LarKC Urban Computing Use Case

� Urban Computing to address cities' challenges� LarKC to elaborate urban data and provide solutions

Is public transportation where the people are?

Which landmarks attract more people?

Where are people concentrating?

Where is traffic moving?


"Semantics" in the LarKC Urban Computing use case

Semantic Technologies

� Semantic Points of Interest (POIs) retrieval:

� Which are the relevant monuments? What events are scheduled tonight? What POIs should I visit?

� Operations Research:

� What is the most "desirable" path to my destination (shortest vs. quickest)? How should I schedule my city tour given my time constraints (TSP problem)?

� Machine Learning:

� What POIs could be interesting for me? Should I expect traffic on my way to…? Which "mavens" of this POI should I ask for advice?

� RDF/SPARQL/OWL based interoperability:

� integration and interoperability between different components through the adoption of Semantic Web technologies (enabled by the LarKC platform)


� Year 1:� Urban LarKC: find monuments and events in Milano and the

shortest path to reach them

LarKC Urban Computing Demonstrators


� Year 1:� Glusic: music concert recommender system based on social

streams and multimedia contents



� Year 2:� Korean Road Sign Management: automatic checking of road signs

information in Seoul



� Year 2:� Traffic LarKC: find the shortest/quickest path to your destination in

Milano with traffic predictions - http://larkc.cefriel.it/traffic-larkc



� Year 3:� BOTTARI: Location-based Social Media Analytics on Seoul POIs



� Year 1:� Urban LarKC: find monuments and events in Milano and the shortest

path to reach them� Glusic: music concert recommender system based on social streams

and multimedia contents

� Year 2:� Korean Road Sign Management: automatic checking of road signs

information in Seoul� Traffic LarKC: find the shortest/quickest path to your destination in

Milano with traffic predictions

� Year 3:� BOTTARI: Location-based Social Media Analytics on Seoul POIs� Semantic City Tour Planner: Travel Salesman Problem with Time

Windows (TSP-TW) with dynamic Semantic POI retrieval in Amsterdam



What's next today?

� Semantic City Tour Planner: Travel Salesman Problem with Time Windows (TSP-TW) with dynamic Semantic POI retrieval in Amsterdam� Master Thesis work of Chris and Bas from the Vrije Universiteit

Amsterdam � Participating to the LarKC Workflow Challenge

http://www.larkc.eu/larkc-workflow-challenge-2011/

� Traffic LarKC: find the shortest/quickest path to your destination in Milano with traffic predictions� Collaboration between CEFRIEL and Siemens in LarKC� Winner of the AI Mashup Challenge 2011

https://sites.google.com/a/fh-hannover.de/aimashup11/


Thanks for your attention! Any question?

Contact: Irene Celino – Semantic Web Practice

CEFRIEL – ICT Institute, Politecnico di Milanoemail: [email protected] – web: http://swa.cefriel.it

personal website: http://iricelino.org

phone: +39-02-23954266 – fax: +39-02-23954466

slides available at: http://www.slideshare.net/iricelino

Introduction to Urban Computing in LarKC – Cefriel, 28th June 2011

Introduction to Urban Computing in LarKC

Technology

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