BIG DIVE 7th edition

June 18th to July 13th 2018 | Turin, Italy

 

SEVENTH EDITION DIVERS LIST


/ MUHAMMAD AL READEAN
/ LUCA BARBATI
/ STEFANO CALDERAN
/ OMJYOTI DUTTA
/ PIER PAOLO GRASSI
/ MAREK KUFEL
/ ENRICO LOMBARDO
/ MARCO MARTELLACCI
/ STEFANO MENOZZI
/ DANIELE MORANO
/ MICHELE MORELLO
/ IVAN NARDINI
/ GABRIELE PECE
/ ELISA REALE
/ MASSIMO SANTOLI
/ MARCO SEBASTIANELLI
/ CHRISTIAN TORRERO

TEACHERS & SPEAKERS


Renato Gabriele – oohmm.info
Fabio Franchino – todo.to.it
Stefania Delprete – top-ix.org
Alessandro Molina – axant.it
Niccolò Bidotti – agilelab.it
Sarah Wolf, Andreas Geiges – globalclimateforum.org
Salvatore Iconesi, Oriana Persico – he-r.it
Simone Marzola – ovalmoney.com
Isabella Iennaco, Paolo Ranieri – knowage-suite.com
Andrè Panisson, Alan Perotti  – isi.it
Alexandre Lissy – mozilla.org
Maurizio Napolitano – fbk.eu

Guest speakers
(in collaboration with ISI Foundation)
Roberta Sinatra – robertasinatra.com
Michael Sxell – michael.szell.net

DATA SPONSORS


Vem Solutions
vemsolutions.it

SmartData@PoliTO
smartdata.polito.it/

 

OTHER DATA PROVIDERS


INRIM
inrim.eu

NIST
comune.torino.it

SMART DATA NET
smartdatanet.it

5T Torino

5t.torino.it

FINAL PROJECTS

Group 1) “The Group”
Data by: Vem Solutions

 

Team Group: E. Lombardo, C. Torrero, P. P, Grassi, L. Barbati, S. Menozzi
Team Composition: Developers: x2 | Data Scientists: x2 | Domain Expert: x1

The question behind the project work: considering the 2% of vehicles population, is it possible to extract key trends in parking and point of interests?

The group explored mobility in Turin using dataset by Vem. They started by visualizing both in a static and a dynamic way the flows. Then they focused on anomalous situations in vehicles distribution trying to link them to mainstream city events.

After this analysis the team stated that Vem blackboxes provide a complete characterization of the points of interest around the city.

Tools: MySQL, Flask, PowerBI, QGIS, Jupyter Notebook. deck.gl framework
Data Science Methods: Kernel Density Estimation, Grid Clustering

Group 2)“M2O”
Data by: Vem Solutions, INRIM

Team Group: M. Morello, O. Dutta, M. Al Readean
Team Composition: Developer: x1 | Data Scientist: x1 | Researcher: x1

The question behind the project work: is it possible to correlate “noise” (optical fiber oscillation) to the traffic distribution over the city ?

Recent studies proved that optical fiber oscillation measurement can be used to detect earthquake phenomena. This is particularly relevant in the submarine geo-seismic events where it is difficult to use traditional seismographs. Using a similar approach and crossing data from Vem and INRIM, the group found an interesting correlation between the fiber noise and the general behavior of vehicles around the city.

Tools: QGIS, Power BI, Python and R, MS Access, Jupyter Notebook
Data Science Methods: Moving Averages, Linear Regression

Group 3)“TOnnect”
Data by: SmartData@PoliTO

Team Group: G. Pece, I. Nardini, E. Reale, M. Sebastianelli, M. Santoli,
Team Composition: Data Scientists: x2 | Researchers: x2 | Developer: x1

Behaviors and trends identification of car-sharing mobility between Turin and Caselle Airport, and between Milano and Linate Airport analyzing data provided by SmartData@PoliTO.

The team proceeded developing geospatial clustering and time series prediction of flow-in and flow-out in a specific cluster. For improving the user experience the team proposes a mobile app to organize and visualize the different choices available in real-time to reach the airport.

Tools: Python and libraries, Jupyter Notebook, deck.gl framework, Adobe Illustrator
Data Science Methods: DBSCAN and HDBSCAN algorithms, ARIMA, AUTOARIMA, PROPHET

Group 4)“Four Pandas”
Data by: Vem Solutions

Team Group: S. Calderan, D. Morano, M. Kufel, M. Martellacci
Team Composition: Developers: x2 | Data Scientist: x1 | Researcher: x1

Traffic level prediction in Turin studying Vem Solution mobility dataset by analysing the metrics in specific areas of the city.

After researching for a metric for the traffic level and frequency, the team created a grid dividing the city in 500 zones and carefully choose the algorithm for to optimise their prediction of the traffic.

Tools: Python and libraries, Jupyter Notebook, JS
Data Science Methods: Extremely Randomized Trees Regressor, Extreme Gradient Boosting (XGBoost)