Big Data challenges to foster AI research and applicationsaiia2014.di.unipi.it/_media/bigdata/bigdataaiia2014_antonelli.pdf · Big Data challenges to foster AI research and applications

Big Data challenges to foster AI research and applications

GRUPPO TELECOM ITALIA Workshop on Embracing Potential of Big Data Pisa, 12 Dicembre 2014

Fabrizio Antonelli – SKIL Lab

350 young people (200

in the innovation

area)

JOLs in brief: 5 universities involved in the first phase (Polytechnic University of

Turin, Polytechnic University of Milan, Trento University, Sant’Anna School of Advanced Studies in Pisa and Catania University)

Interdisciplinary teams focusing on university areas of excellence "Open” research at international level in collaboration with

organisations such as the European Institute of Technology (EIT) and Massachusetts Institute of Technology (MIT)

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8 JOLs within 5 Italian universities of

excellence

Joint Open Labs are research and innovation laboratories set up within university centres, as a result of partnerships and agreements between Telecom Italia and the major Italian universities in specific fields of scientific and technological interest.

13 million euros

invested (2012 to

2015) 15% thesis /

internships 25% PhDs

funded within JOLs

The Joint Open Labs of Telecom Italia

Big Data challenges to foster AI research and applications Fabrizio Antonelli, SKIL Lab

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Semantics & Big Data Mobile Smart Spaces Robotics

Multimedia

Internet of Things

Mobile Social Platforms

Mobile devices Lab

TO MI TN

PI

E-Health and Wellbeing

Joint Open Labs throughout Italy


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Challenges around the World 21st Century Grand Challenges

http://www.whitehouse.gov/administration/eop/ostp/grand-challenges


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Orange D4D Challenge

Challenges around the World

http://www.d4d.orange.com/en/home


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Censimenti Data Challenge


http://censimentoindustriaeservizi.istat.it/istatcens/censimenti-data-challenge-il-contest-sui-dati-del-censimento/


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Piemonte Visual Contest


http://www.piemontevisualcontest.eu


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Motivations

Big Data

Big solutions?


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Motivations

Lack of finding specific competences

Increasing will of participation

The web as the enabler to put in touch demand and offer

Need of engaging the ecosystem


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• Trust in external developers not engaged with traditional processes

• Open up their data, IP, asset (the attendees get enough information that they can create relevant solution for these corporate)

• Regulatory framework constraints

• Internal frictions

• Developing a strategy

The cultural change What prevents from a broader adoption of the challenges as a new paradigm of innovation


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www.telecomitalia.com/bigdatachallenge


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The Telecom Italia Big Data Challenge 2014

Telecom Italia BIG DATA CHALLENGE is an initiative aimed at involving researchers, developers and designers from all the globe on the Big Data.

The CHALLENGE gives the chance to connect with an international network of professionals for collecting ideas and approaches on heterogeneous Big Data exploitation (private data, open data, sensor data, etc.) through the development of APPLICATION, ANALYTICS and VISUALIZATION.


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In collaboration with


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How was it designed?

Telecom Italia BIG DATA CHALLENGE was divided in two phases:

A first phase (2 months long) where individuals or teams can apply for participation and download a DATASET of heterogeneous data (Telecommunication, transportation, weather, etc.) referred to a specific period to be used for the development of apps, analytics or visualizations.

A second phase (Big Data Jam), during the ICT DAYS 2014 in Trento*, where in a 2 days of meetings, panels and workshops the participants are invited to present their work and where a committee (made of personalities from the scientific, institutional and industrial world) will reward the best ideas.

* 2013.ictdays.it/it


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How was it structured?

Telecom Italia BIG DATA CHALLENGE was structured in TRACKS. Each participant can apply for a specific track according to her competence or interest.

The available TRACKS are:

1. APP DEVELOPMENT: development of data-oriented application starting from the available data

2. DATA ANALYTICS: data mining for the extraction of correlations, patterns, trends, etc.

3. DATA VISUALIZATION: development of visualizations for the data storytelling.

Big Data challenges to foster AI research and applications

Fabrizio Antonelli, SKIL Lab

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What kind of data?

The participants can download a geo-referred DATASET (period of Nov/Dec 2013) related to two different Italian territories made of several millions records:

The TRENTINO region and the METROPOLITAN AREA OF MILAN


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What kind of data?


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What kind of data?


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The Dandelion data distribution platform (powered by SpazioDati)


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Prizes

• 6000€ for the first selected idea in any of the 3 tracks

Other benefits:

• Exhibition of the visualizations in the MUSE museum and in other EIT ICT Labs nodes

• Special Issue on EPJ Data Science Journal, edited by Frank Schweitzer (ETH Zurich) and Alessandro Vespignani (Northeastern University)


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Numbers

Participants: 1108


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Numbers

100+ submissions 10 finalists 3 winners


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Winning team – APP DEVELOPMENT Project: Living Land Use - Team: LocaliData

Idea – analyse the activity data to elicit land use footprints

The Living Land Use application aims at: 1. Deriving land use "footprints" of

Milano by analysing the "activity data" provided by the Big Data Challenge 2013

2. Comparing the "elicited" land use footprints with the land use classification provided by CORINE in 2009

3. Identifying relevant deviations in land use between 2009 and 2013

Living Land Use - http://livinglanduse.cefriel.com

Milano grid and in-calls footprint for week days/weekend in cell 6060

CORINE land use classification (viz: QGIS, background map: OpenStreetMap)

2009

2013

Construction site


http://livinglanduse.cefriel.com/

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Winning team – DATA VISUALIZATION Project: Human impact from a bird’s eye view - Team: Easystats Ltd


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Winning team – DATA ANALYTICS Project: People as Sensors for Predicting Energy Consumption - Team: University of Trento

The goal is to optimize electric energy producer-distributor-consumer value chain in Trentino province (Italy). ● Predict average daily energy consumption for each line through the

electrical grid of the Trentino province (Italy) based on human behavioral data, derived from mobile phone aggregated and anonymized activity, => thus optimizing the economy of energy producers and distributors value chain and reducing climate change impact.

● Predict peak daily energy consumption for each line through the electrical

grid of the Trentino province (Italy) based on human behavioral data, derived from mobile phone aggregated and anonymized activity, => thus meeting consumer peak demand.


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The Telecom Italia Big Data Challenge have now been released in open source

What’s next

http://theodi.fbk.eu/openbigdata/


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We’ll see you at

Telecom Italia Big Data Challenge 2015!

What’s next


Grazie Thanks

@faberAntonelli [email protected]

Documents

Big Data challenges to foster AI research and applicationsaiia2014.di.unipi.it/_media/bigdata/bigdataaiia2014_antonelli.pdf · Big Data challenges to foster AI research and applications