Mobile Data Mashups for Urban Computing Applications

Copyright 2007

Politecnico di MilanoDipartimento di Elettronica e Informazione

&

Mobile Data Mashups per applicazioni di Urban

Computing

Emanuele Della Valle Irene Celino .

[email protected] [email protected] .

http://emanueledellavalle.org http://swa.cefriel.it .

Joint work with:Daniele Dell’Aglio, Kono Kim, Zhisheng Huang, Volker Tresp, Werner Hauptmann, and Yi Huang

Agenda

• Introduction– Cities are alive– Mobile users’ questions– Urban Computing– Data Mashups

• Are Data Mashups up to address Mobile users’ needs?– Powerful visualization– Simple programming abstractions– Does everything boil down to plumbing?

• Requirements for a Mobile Data Mashup Environment• LarKC as a backbone for a Mobile Data Mashup Environment

– What’s LarKC?– Asking LarKC– Plugging components in LarKC– Configuring a LarKC pipeline– Demo of current state of development of Urban Baby LarKC– What’s next?

• Conclusions and outlooks

2GHOSTWAY Event, Vimercate, Milano 27-5-2009 Emanuele Della Valle and Irene Celino

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


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

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

Urban Computing

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


Models of the city

Sensors

Intelligence

Actuators

Urban, Social, Business and Organizational Context

Example of Urban Computing Application


Models of the city

Sensors

IntelligenceActu

ators

5th cycle ASP Multidisciplinary Projects, Torino 24.1.20097

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 are available on the Internet at almost no cost, e.g.,

– 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...)


Are Data 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/ ]

Data Mashups Offers Powerful Visualizations


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/

Data Mashups Offers Simple Programming Abstractions


Not Everything Boils Down to Plumbing


Citysense was built to show you where the action is, right now.Using a billion points of GPS and WiFi positioning data from the last few years – plus real-time feeds – Citysense sees S.F. from above and puts the top live hotspots in your hand. You don't even need to sign up, just go to citysense.com on your BlackBerry, download, and open.

Can Citysense Be Implemented with Pipes?


• Live overall activity & top hotspotsFirst of all see if it's a good night to go out. The city is 21% busier than normal for right now? Let's go. But where to? Check out the top hotspots in real-time and head out.

• What's at hotspot #1?Click over to Yelp or Google and find out what's going on at the #1 hotspot: Bars? Clubs? Restaurants? Then check out what's at #2

• Show me where the unusually high activity isEven if you're a local, Citysense can give you the live details you need. When the Mission or Soma is busier than normal – you'll know immediately.

• Find out where everyone is goingAfter dinner, drinks or a great night at a club, do you ever wonder where the afterparty is? Just press the "Locate Me" icon and see the top 5 places people go to from where you are now.

[Source: http://www.citysense.com/moreinfo.php ]

Coping with reasoning heterogeneity


It means the systems allow for multiple reasoning paradigms; e.g.

• precise and consistent inference for telling that at a given junction all vehicles, but public transportation ones, must go straight

• approximate reasoning when calculating the probability of a traffic jam given the current traffic conditions and the past history.

[ source http://senseable.mit.edu/ ]

Coping with defaults heterogeneity 1/2

Open World Assumption vs. Close World Assumption• While for the an entire city we cannot assume complete

knowledge, for a time table of a bus station we can


[source: http://gizmodo.com/photogallery/trafficsky/1003143552 ]

Coping with defaults heterogeneity 2/2

Unique Name Assumption• A square with several station for buses and subway can

be considered a unique point for multimodal travel planning, but not when the problem is giving direction in that square to a pedestrian


1 2

29

30

L3L3

Coping with scale

• Although we encounter large scale data which are not manageable, it does not necessary mean that we have to deal with all of the data simultaneously.

• Usually, only very limited amount data are relevant for a single query/processing at a specific application.


[source: http://gizmodo.com/photogallery/trafficsky/1003143552 ]

Coping with changing data

• Periodically changing data change according to a temporal law that can be

– Pure periodic law, e.g. every night at 10pm Milano overpasses close.

– Probabilistic law, e.g. traffic jam appear in the west side of Milano due to bad weather or when San Siro stadium hosts a soccer match.

• Event driven changing data are updated as a consequence of some external event. They can be further characterized by the mean time between changes:

– Slow, e.g. roads closed for scheduled works

– Medium, e.g. roads closed for accidents or congestion due to traffic

– Fast, e.g. the intensity of traffic for each street in a city


The LarKC Project


The Large Knowledge Collidera platform for infinitely scalable reasoning on the data-web

Pip

elin

e

LarKC at work for Urban Computing 1/2


The Large Knowledge Collider project aims to develop a platform for massive distributed incomplete reasoning

Models of the city

Sensors

IntelligenceActu

ators

Traffic Monuments

We are combining route planning techniques with reasoning on symbolic knowledge and traffic prediction produced by recurrent neural networks and continuous estimation of residual road capacity by real time analysis of data streams

Inductive Loop

http://www.larkc.eu

PROBLEM: Which Milano monuments can I quickly visit from here?

LarKC at work for Urban Computing 2/2

• We are combining route planning techniques with

– reasoning on symbolic knowledge,

– traffic prediction produced by recurrent neural networks, and

– continuous estimation of residual road capacity by real time analysis of data streams


You are

here

Conclusions and Outlooks

• LarKC aims at becoming an experimentation infrastructures for the development of advance semantic technologies.

• The public launch of the first open source release of the platform will take place in June 2009

• We are developing our Urban Computing application as a showcase of the potentiality of the LarKC platform


The Large Knowledge Collider a platform for massive distributed incomplete reasoning

http://www.larkc.eu

Thank you for paying attention


Any Questions?

Copyright 2007

Politecnico di MilanoDipartimento di Elettronica e Informazione

&

Mobile Data Mashups for Urban Computing

Applications

Emanuele Della Valle Irene Celino .

[email protected] [email protected] .

http://emanueledellavalle.org http://swa.cefriel.it .

Joint work with:Irene Celino, Daniele Dell’Aglio, Kono Kim, Zhisheng Huang,

Volker Tresp, Werner Hauptmann, and Yi Huang

Identifier strategy for Pipeline 2B

• Strategy based on common sense behavior:– Detailed graph around starting and destination point (circles

with center in the points and radius of 250 m)– Main roads of the city

• Implemented in MixedStrategyIdentifier


Towards Urban Baby LarKC Pipeline 3

Urban CityDecider

Urban CityDecider

SPARQLResult

SPARQLResult

SPARQL Query

SPARQL Query

LocalPlug-in Manager


SPARQL to GeoQuery

Transformer

SPARQL to GeoQuery

Transformer

Plug-in APIPlug-in API



SPARQL to GeoQuery

Transformer

SPARQL to GeoQuery

Transformer




Geo LocationIdentifier










Growing Data Set Selector

Growing Data Set Selector




PathFindingReasoner

PathFindingReasoner




SPARQLEndpointIdentifier

SPARQLEndpointIdentifier



Adding Traffic Predictions


B1

calendar inputt, #24external inputt, #4

A

B2 B3

calendar inputt+1, #24external inputt+1, #4

hidden, #8

C1

traffict, # 32

0-8h

hidden, #8

C2

traffict, # 32

8-16h

hidden, #8

C3

traffict, # 32

16-24h

hidden, #8

traffict+1, # 32

0-8h

C1

hidden, #8

traffict+1, # 32

8-16h

C2

hidden, #8

traffict+1, # 32

16-24h

C3previous

daynextdayA A A A

B1B2 B3

• Goal: Short and Mid-Term Traffic Flow and Speed Forecast

• Neural Network Architecture:– We use a time-delay recurrent neural network to forecast the traffic flow and speed– The neural network constructs a minimal set of indicators containing the traffic structure.

• Proceeding:– Data: traffic data (flow and speed) and external inputs (e.g. temperature, holydays)– Perform feasibility study to work out specific (prototype) neural network forecast models– Develop demonstrator for traffic flow and speed forecasting based on prototype

Dealing with Streaming Data

• To deal with streams in the Semantic Web context we defined C-SPARQL an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed.

• An example of C-SPARQL queryREGISTER STREAM CarsEnteringCityCenterPerDistrictCOMPUTED EVERY 5 MIN AS

PREFIX c: <http :// linkedurbandata . org/ city #>PREFIX t: <http :// linkedurbandata . org/ traffic #>

CONSTRUCT {? district t:has - entering - cars ? passages }

FROM STREAM <http :// stream . org/ milantollgates .trdf >[ RANGE 30 MIN STEP 5 MIN ]

WHERE { ? tollgate t: registers ? car . ? district c: contains ? street . ? tollgate c: placedIn ? street . }AGGREGATE {(? passages , COUNT , {? district })}


STREAM/CQL

RDFKnowledge Base

⋈

S1

S2

Data Streams

WHEREBindings

⋈

StreamingStatic

Stream Manager

Stream Transcoder

AGGREGATESBindings

RDF Data Streams

pCONSTRUCTDESCRIBE

QUERYASK

Variable BindingsRDF TriplesResult Projection

Sesame/SPARQL

FILTER

FILTER

Static RDF Data

Aggregation

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Mobile Data Mashups for Urban Computing Applications

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