DSM 2020 : FIRST INTERNATIONAL SUMMER SCHOOL ON DATA SCIENCE FOR MOBILITY

AIM OF THE SCHOOL

Massive amounts of spatio-temporal data representing trajectories of moving objects are produced by an ever-increasing number of diverse, real-life applications, ranging from mobile to social media apps and surveillance systems, from vehicle tracking systems to IoT mobile sensors. Such mobility-aware traces come in huge numbers and very complex forms, and can be enriched with multiple different semantic dimensions. These semantically enriched trajectories have the potential to unveil novel challenges in several domains, such as urban, maritime and aviation.  

The explosion in Data Science is happening now. The Big Data technological infrastructure has reached maturity. Significant interest from the research community is being shown towards the Big Data Value Analytics reference model: data management, data processing, data analytics, data visualization. The time is right for the field of Mobility Data Science to follow the trend!

Our First International Summer School on Data Science for Mobility offers participants both visionary keynote speeches and hands-on mini courses held by leading experts in AI and Data Analytics for Mobility from Canada, Greece & Italy. The keynotes speeches will explore the challenges faced due to the voluminous and complex mobility data generated every day in maritime and aviation domains. The hands-on mini courses complement the keynotes by giving practical experience in the usage of analysis tools on real mobility datasets.

TARGET GROUPS

This Summer School is intended for PhD students, researchers and practitioners in the fields of Computer and Information Science, interested in learning about the most recent developments in mobility data science.Attendees will familiarise themselves with the most recent data science trends, including deep learning and AI methods for mobility data, methods to analyse human mobility as well as with methods for big mobility data. With the hands-on experience participants will gain familiarity with some commonly used tools and datasets.

COURSE AIM

At the end of the course, each attendee will:

• Understand how to analyse mobility data with deep learning techniques
• Understand how machine learning and AI methods can be tailored to mobility data
• Understand how to manage Big Mobility Data
• Gain significant hands-on experience with state-of-the-art technologies and tools
• Have a vision of open research as well as technological challenges customized to key application areas and domains