<h3><span class="cef-display-3 cef-date-day"><strong>23</strong><span class="cef-h6 cef-date-month">Feb</span></span></h3>
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<h4 class="cef-h5"><strong>3 PM CET</strong></h4>
<p>Introduction to CEF <br>// By Erbil Kop - Onboarding Manager</p>
<p> Brief introduction on CEF Digital and its Building Blocks including BDTI </p>
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<h4 class="cef-h5"><strong>3.05 PM CET</strong></h4>
<p>BDTI in a nutshell <br>// By Andrea Biancini - Project Manager</p>
<p> An introductory overview of what is BDTI, and which are its main features and benefits.</p>
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<h4 class="cef-h5"><strong>3.15 PM CET</strong></h4>
<p>BDTI Service Offering and how to get started <br>// By Roberta Talarico - Communication expert <br>// By Federica Russo - Service and UX Designer </p>
<p>Presentation of the BDTI Community Portal focusing on the purpose and main goals of the platform. An overview of the portal will be shown through a navigation session.</p>
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<h4 class="cef-h5"><strong>3.30 PM CET</strong></h4>
<p>Municipality of Milan Pilot Showcase <br>// By Elisa Bellistri - SIAD Management Instructor, Analytics and Data Science Unit, Municipality of Milan </p>
<p>The Municipality of Milan will present its BDTI pilot sharing its objectives and experience, with a focus on its role and expectations withwithin the BDTI Community Portal.</p>
<p>"The Municipality of Milan takes part intoin the BDTI project in order to manage different scenarios during the ongoing pandemic emergency. In particular, resultsthey aim to make forecastforecasts about movementsthe movement of people, related to citizens through the city, based on the opening hours of stores, industries, schools and other activities. TheMaking these predictionsorts of flows,predictions relatedmakes to different parameters, increases the ability it easier to monitor the city and make takedata-driven decisions.
about city management. The BDTI platform provides an environment to integrate different data from different sources, such as flowsflow information and attributes, historical and real -time data, georeferenced data, population data, and. It is then possible to build advanced models of data prediction."</p>
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<h4 class="cef-h5"><strong>3.45 PM CET</strong></h4>
<p>BDTI Self-paced labs: descriptive analytics, big data and AI <br>// By Ze Wen Wu - Cloud Engineer</p>
<p>Presentation of the Self-paced labs that will be available for each pilot user on BDTI, in order to provide users with basic skills in descriptive analysis, machine learning and big data tools</p>
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<h4 class="cef-h5"><strong>4.05 PM CET</strong></h4>
<p>BDTI Technical architecture & future vision <br>// By Kasper Rutten - Cloud Engineer</p>
<p>Layout and visuals of current architecture, with a quick rundown of features to come in the near future</p>
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<h4 class="cef-h5"><strong>4.15 PM CET</strong></h4>
<p>Q&A + Interactive session </p>
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