The European Big Data Value Forum (EBDVF) 2024 took place from 2 to 4 October in Budapest, Hungary. The three-day conference, organised by the Big Data Value Association (BDVA), in collaboration with AI&AUT EXPO, Eötvös Loránd University (ELTE), Ideal-ist, Neumann Technology Platform and the Institute for Computer Science and Control (SZTAKI), brings together industry professionals, business developers, researchers and policy-makers from all over Europe and other regions of the world to advance policy actions and industrial and research activities in the areas of Data and AI. This year, EMERALDS decided to join the EBDVF as part of the DataNexus cluster.
Under the theme 'Europe for global leadership in AI & Data', the EBDVF 2024 featured 71 workshops, sessions and plenaries bringing together 33 keynote speakers and almost 200 speakers. The DataNexus cluster contributed to these impressive numbers with a 90-minute session entitled 'Innovative Approaches to Extreme Data Challenges'.
This session and the cluster played an essential role during EBDVF in addressing a topic still uncharted, even to the big data and AI community: extreme data and its challenges. "The DataNexus is a very interesting combination of projects working on addressing challenges on extreme data. I think this will give us a very nice result, bringing the whole community together around Extreme Data. We expect a lot from this cluster", stated Savvas Rogotis, Data Ecosystem Senior Project Manager at BDVA, and the moderator of the session.
The session consisted of two parts, aimed at achieving an understanding of how each of the six projects combines innovative technologies and tools for data mining, analysis and visualisation to create robust frameworks that can tackle Extreme Data challenges. First, there was a round of presentations entitled 'New technologies for handling and exploiting extreme data and extreme data use cases'. Here, each project representative explained to the audience gathered in the room how their project addresses the challenges of extreme data and what makes their project unique. Finally, there was a panel discussion followed by a round of Q&A moderated by Savvas Rogotis.
In the case of EMERALDS, Yannis Theordoridis from University of Piraeus and technical coordinator of EMERALDS, participated in the round of presentations. Yannis presented the EMERALDS project as its whole, and its goal to create an urban data-focused Mobility Analytics-as-a-Service (MAaaS) which is efficient, interoperable and easy-to-deploy, consisting of the ‘emeralds’ services. Our technical project coordinator dived deep into the EMERALDS use cases of The Hague, Rotterdam and Riga, even after the session.
"Our idea is to develop a Mobility AI as a Service ecosystem where we will have data processing services, data analytics services, all at the computing continuum, from Edge to Fog to Cloud. This is important because the volume, velocity, and variety of this data are very important for making decisions about where and how it will be processed. We have three use cases on this subject. We have a use case in The Hague, where the idea is to forecast events and crowd density, along with 'what if' scenarios. The second use case is in Rotterdam, where we are working on different scenarios for multimodal traffic management. The third use case is in Riga, where the goal is to study public transportation and create useful scenarios for the public transportation policymakers in that town."
Additionally, Yannis praised the DataNexus Cluster for the synergies it is fostering and strengthening.
"I am sure that with the help of the DataNexus Cluster we will strengthen our footprint in this data ecosystem."
The other speakers in the session were Pedro García from University Rovira i Virgili representing NEARDATA, Stephan Hachinger from LRZ representing EXA4MIND, Radu Prodan from Institute of Information Technology, University of Klagenfurt representing Graph-Massivizer, Xinxin Wang from Wageningen Food Safety Research representing EFRA, and Doaa Almhaithawi from Mathema representing EXTRACT.
