https://www.dagstuhl.de/22021

09. – 14. Januar 2022, Dagstuhl-Seminar 22021

Mobility Data Science

Organisatoren

Mohamed Mokbel (University of Minnesota – Minneapolis, US)
Mahmoud Sakr (UL – Brussels, BE)
Li Xiong (Emory University – Atlanta, US)
Andreas Züfle (George Mason Univ. – Fairfax, US)

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Dokumente

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Programm des Dagstuhl-Seminars [pdf]

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Motivation

Due to the proliferation of handheld GPS enabled devices, spatial and spatio-temporal data is generated, stored, and published by billions of users in a plethora of applications. By mining this data, and thus turning it into actionable information, the McKinsey Global Institute projects a ``$600 billion potential annual consumer surplus from using personal location data globally''. Multiple communities, in computer science, outside computer science, and in industry, have responded to the pertinent challenges and proposed solutions to individual problems. These communities include moving object databases, mobile data management, spatial data mining, geography, urban planning, transportation, spatial privacy, and spatial epidemiology. Integrating these communities around the common interest of mobility data science is the best chance to achieve impactful end-to-end solutions to real life problems.

The goal of this five-day Dagstuhl Seminar is to create a new research community of mobility data science in which whole is greater than the sum of its parts. It will bring together established leaders as well as promising young researchers from all fields related to mobility data science. Related fields include mobility data acquisition, data quality, data management, data analysis, privacy, and applications. Currently, these research fields are largely working independently from each other solving individual problems, with less focus on the integrated end-to-end solutions. The lack of integration remains a barrier, such that little of these results have come to real world use. Therefore, it is timely to introduce the term Mobility Data Science as a domain that seeks integrated data-to-insights solutions, rather than solutions of point problems.

The goal is to exchange the knowledge of the different communities, align knowledge with the needs of participants from industry, and to discuss the integration vision, opportunities and challenges. Topics of the seminar will include:

  1. Requirements for Mobility Data Science (applications, challenges, visions, collaborations);
  2. Data Acquisition and Preparation (datasets, models, data integration, data quality, uncertainty);
  3. Data Analysis (data mining tasks, result evaluation, decision making, privacy);
  4. The Mobility Data Science Ecosystem (towards industry-ready tools, requirements, standards);
  5. Mobility Data Science against Pandemics (contact tracing, simulation, prediction, prevention).

Seminar participants will discuss and collaborate towards the following seminar outcomes:

  1. A Research Agenda: that defines a new research field of Mobility Data Science
  2. A Mobility Data Science Curriculum: To train a new generation of data scientists
  3. Joint Project Initiatives: To foster multi-disciplinary and global collaborations
  4. A Mobility Data Science Ecosystem: Towards open source tools and systems
  5. A Collection of Abstracts, Presentations, and Videos: To disseminate mobility data science

To achieve these outcomes, the seminar will be structured in three parts: Meet, Know, and Do. The Meet part (1 Day) will maximize interaction by having participants give 5-minute self-introductions followed by a “speed dating” session where participants will be divided into groups. The Know part (2 Days) will feature presentations and tutorials by experts followed by discussions of visions and challenges. The Do part (2 Days) will have participants leverage ideas and synergies by working in groups on the above seminar outcomes.

This seminar is nicely co-located with the Dagstuhl Seminar "Mobility Data Analysis: from Technical to Ethical" to be held from Sunday, January 9 to Wednesday, January 12, 2022, organized by Bettina Berendt (TU Berlin, DE), Stan Matwin (Dalhousie University – Halifax, CA), and Chiara Renso (ISTI-CNR – Pisa, IT) with which we plan to hold shared sessions and moments of interaction. Participants will have the chance to simultaneously participate in two seminars.

Motivation text license
  Creative Commons BY 4.0
  Mohamed Mokbel, Mahmoud Sakr, Li Xiong, and Andreas Züfle

Classification

  • Databases
  • Machine Learning
  • Other Computer Science

Keywords

  • Data science
  • Mobility
  • Contact tracing
  • Transportation
  • Privacy

Dokumentation

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Download Übersichtsflyer (PDF).

Publikationen

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