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MARVEL Workshop - DATAWEEK2022 - The challenges of the extreme-scale multi-modal analytics applications

Authors: Sotiris Ioannidis; Atzori, Manfredo; Passalis, Nikolaos; Figueiras, Paulo;

MARVEL Workshop - DATAWEEK2022 - The challenges of the extreme-scale multi-modal analytics applications

Abstract

Data Week 2022 was held both online (May 25th to June 3rd) and physically (June 8th – 9th) this year. Data Week is the spring gathering of the European Big Data and Data Driven AI research and innovation communities, where the participants share knowledge and results, discuss topics of common interest, find synergies, build new collaborations and identify new challenges and recommendations. Data Week is co-organized by Big Data Value Association and the EUHubs4Data project. This year’s main theme was “Towards an innovative, trusted and fair European data economy”. On Wednesday, May 25th, MARVEL’s Project Coordinator Prof. Sotiris Ioannidis e-welcomed Dr. Manfredo Atzori, Scientific Coordinator of ExaMode, Dr. Nikolaos Passalis, Technical Manager of OpenDR, and Dr. Paulo Figueiras, senior Researcher of VesselAI at a workshop where they explored “The challenges of the extreme-scale multi-modal analytics applications” in the context of the research projects they participate. The session explored the latest EU research outcomes related to Extreme Data Analytics applications in smart cities, healthcare, robotics, and the maritime industry. The participants focused on the key challenges addressed in each sector in an effort to showcase the lessons they learned the hard way and encourage other researchers to take them into consideration. Find out more about everything discussed by watching the recording of the session here.

Keywords

workshop, ExaMode, VesselAI, MARVEL, OpenDR, DATAWEEK

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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