3cixty - A New Platform for City Exploration

3cixty: a New Platform
for City Exploration
Raphaël Troncy <raphael.troncy@eurecom.fr>
Multimedia Semantics, EURECOM
@rtroncy
… and many others who need to be credit:
Giuseppe Rizzo, Houda Khrouf, Julien Plu, Ahmad Assaf,
Oscar Corcho, Juan Carlos Ballesteros,
José Luis Redondo Gardia, Ghislain Atemezing, Vuk Milicic,
several students projects, etc.

07/07/2015 - BMW Summer School - Lake Tegernsee - 2

The Central Challenge

An Evening in Milan

“I’d like to take
a break from
Expo and visit
Milan. What’s
the best time
for a break,
and what
things in the
city could I go
to then?”
Expo and the City

Our Solution: Apps Powered by 3cixty
Showcase app: ExplorMI 360 https://www.3cixty.com/

From Technology Maturation To Business
 First showcase in Milan (2014-2015)
 Next showcases planned in London, Nice,
Bologna, Madrid (2015-2016)

3cixty: a smart city and big data project

 Events
 Places and Sights
 Reviews and Ratings
 Media
 Transport
 Urban Summaries

… and the Web

A lot of information…
http://www.flickr.com/photos/mwparenteau/432039783
07/07/2015 - BMW Summer School - Lake Tegernsee

Searching for Tegernsee (Bavaria)

Searching for Milan (Italy) or EXPO 2015
https://en.wikipedia.org/wiki/
Knowledge_Graph

Google Knowledge Graph

Bing - Satori

wikidata:Q210022dbpedia:Expo_2015
- 17

Linked Data Principles
 Tim Berners Lee [2006] (Design Issues)
1. Use URIs to identify things
(anything, not just documents);
2. Use HTTP URIs
– globally unique names, distributed ownership –
so that people can look up those names;
3. Provide useful information in RDF –
when someone looks up a URI;
4. Include RDF links to other URIs –
to enable discovery of related information
- 18

3cixty Architecture
Heterogeneous data sources
Data Crawling
Data Streams
RDF Conversion
RSS Update
- 19

Knowledge Modeling: Transit

Knowledge Graphs for Transportation

Knowledge Graphs for Places / Events

On the Importance of Having Good Maps

3cixty: a smart city and big data project

Handling Highly Dynamic Data
 Data streams
 Live hotel rooms availability through the EAN network (Expedia)
 Live position of the city buses in the city
 Live state of bike sharing stations
 Complex Event Processing: T-Rex / SPARQL Streams
T-Rex
TESLA
rules
Publishers Subscribers
Primitive
events
Primitive or
comp. events
E015 pull-push
adapter
E015
services

CEP with T-Rex
 T-Rex is a high-performance CEP engine, providing an ad-
hoc rule language (TESLA)
 Examples of TESLA rules:
Define GrowingDelay(t_id: string, delay: int)
From TrainDelay(t_id => $t, delay => $d) as T1 and
last TrainDelay(t_id=$t, $d>delay) as T2 within 5min from T1
Where GrowingDelay.t_id := T1.t_id, GrowingDelay.delay:=T1.delay
Define LowBikeAvail(bike_stat: string, availNow: int, availAvg: int)
From BikeMiStat(avail_bikes<5) as S
Where bike_stat := S.stat_id, availNow := S.avail_bikes,
availAvg := AVG(BikeMiStat.avail_bikes) within 60min from S

3cixty Architecture
Real-time Reconciliation
- Category mapping
- Instance matching
Data Crawling
Data Streams
RDF Conversion
RSS Update
- 29

Are those two venues the same?
Google Places: name, address,
geoLoc
Yelp: name, address,
geoLoc
➔ Slightly different POI name
➔ Different tel number
➔ Different locality (Milan or
Pero?)
➔ Different region (Lombardia
or MI?)
➔ Distance using the
Harvesine formula: 2,428m !
- 30

 The events similarity is a mutual agreement of their factual properties
 Based on top-k dependencies between properties
Data reconciliation (learning to align)
p1 p2
dependency
title1 title2 0.30
place1 place2 0.28
title1 agent2 0.26
agent1 agent2 0.21
description1 title2 0.16
Minimal conditions to fetch
similar events using SPARQL
1st level
Refine the results
2nd level

3cixty Architecture
Real-time Reconciliation
- Category mapping
- Instance matching
Data Crawling
Data Streams
RDF Conversion
RSS Update
- 32
Web Applications
SPARQL
REST API (Elda)

ExplorMI 360 Web App

ExplorMI 360 Mobile Guide (Android / iOS)

Bringing your Wishlist with you

 A Service for Executing Mixed-Domain Queries
Execute queries in your app that combine diverse types
of information
 A “Parallel Exploration” Graphical UI
Enable users to construct and save trees of interrelated
queries that enable them to explore several aspects of
the city simultaneously
 A “Wish List” Service
Allow users to indicate where they may want to go and
(optionally) when
Store this information in the cloud so that it can be
accessed from any 3cixty app when the user is logged in
Brief Descriptions of Services (1)

Brief Descriptions of Services (2)
 A Mobility Profiling service
Track users’ movements within the city, including their
use of modes of transportation
Enable users to make queries with restrictions like “in a
location that I’ve never been to before”
Display data from the user’s mobility profile along with
other 3cixty information
 Generic Crowdsourcing Platform
Efficiently write effective interfaces that enable users to
contribute information about aspects of the city
access such information provided by others
 A Social Network Mining Service
Access what a user’s friends have done or said with
regard to particular things in the city

Mobility Profiling Service

Data Mining in the Knowledge Graph

Extracting Patterns in the Knowledge Graph
 Geosummly:
http://geosummly.eurecom.fr/
Zooming in “Shop & Service”

1 2
n
lat*,long* = latitude and longitude of the centroid of the cell(i). It allows to reduce the
observation noise of the single venues, and to reduce the data set sparsity.
1 lat* lng* f(1,1) f(1,2
)
2
3
...
n f(n,n)
Grid Sampling on Foursquare

Definitions:
 eps : reachable distance. We use the
Euclidean distance (points linked
with arrows)
 minPts : min number of points to
have a cluster (given the example, it
can be 1...8)
Automatic parameter estimation:
 eps : applying the Euclidean
distance, we compute the mean
Euclidean distance of the feature
values in the grid
 minPts : considering each feature
independent each other, we assign
to minPts the mean value observed
in the entire grid, reduced a small
quantity based on the law of large
numbers
lng
lat
Parameter Estimation

Clustering Algorithm
 We propose GeoSubClu, a density based clustering
algorithm inspired by SubClu
 Inputs:
 eps, minPts
 O = {o1 , . . . , om} of geographic objects located at the spatial
coordinates fx and fy. Each object corresponds to the centroid of a
cell in the sampled grid
 F = {f1, …, fn} set of features. Each feature corresponds the
observed frequency of a given category in the area, normalized
intra-feature and per surface of the cell
 Outputs:
 S = {s1, …, sk} of k-dimensional subspaces sk. Each subspace
corresponds to a cluster that has k prominent different features

Extracting and Linking Entities
 An hybrid approach which combines the strength of a
linguistic-based method augmented by a high coverage
in the annotation obtained by using a large knowledge
base

Extracting More Information from Reviews
 Extracting sentiments: features engineering
Pre-processing, emotion dictionary (DAL), POS,
capitalization, time of the day, day of the week, weather,
social graph
SVM + kNN classifiers used jointly
 Extracting
sub-categories
Japanese restaurant
Ramen or Teppanyaki
LDA approach
BoW with 1-ngram

Semantic and Machine Learning
Lise Getoor - Combining Statistics and
Semantics to Turn Data into Knowledge
ESWC 2015 Keynote

Big Data is not Flat
 It is multi-modal, multi-relational, spatio-
temporal, multimedia
Machine Learning needs knowledge graphs
Knowledge Graphs needs machine learning
Deep learning vs Features Engineering
 Key idea: Statistical Relational Learning (SRL)
Entity Resolution: determine which nodes refer to the
same underlying real world object
Link Prediction: infer the existence of new edges in the
graph
Classification: infer labels in a graph

http://www.slideshare.net/troncy

3cixty - A New Platform for City Exploration

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