Euro-Par 2024 | Track 4: Data analytics, AI, and Computational Science | International European Conference on Parallel and Distributed Computing Track 4: Data analytics, AI, and Computational ScienceEuro-par

Track 4 - Data analytics, AI, and Computational Science

Chairs

Program Committee

  • Rizos Sakellariou, University of Manchester
  • Sukhpal Gill, Queen Mary University of London
  • Bartosz Balis, Institute of Computer Science & ACC CYFRONET AGH, Krakow
  • Christos Baloukas, National Technical University of Athens
  • Julián Arenas-Guerrero, Universidad Politécnica de Madrid
  • Suren Byna, The Ohio State University
  • Barbara Cantalupo, University of  Torino
  • José M Cecilia, Universitat Politècnica de València
  • Tania Cerquitelli, Politecnico di Torino
  • Alexandru Costan, INRIA
  • Hao Dai, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
  • Pedro Diniz, FEUP, University of Porto
  • Reza Farahani, University of Klagenfurt
  • Philipp Gschwandtner, University of Innsbruck
  • Shadi Ibrahim, Inria, Rennes Bretagne Atlantique Research Center
  • Hideyuki Kawashima, Keio University
  • Youngjae Kim, Sogang University
  • Dalibor Klusacek, CESNET, Brno, Czech Republic
  • Michael Kuhn, Otto von Guericke University Magdeburg
  • Young Choon Lee, Macquarie University
  • Manolis Marazakis, Instutute of Computer Science, FORTH
  • Jorji Nonaka, RIKEN Center for Computational Science
  • Ramon Nou, Universitat Politècnica de Catalunya
  • Dana Petcu, West University of Timisoara
  • Jagat Pudipeddi, META
  • M. Mustafa Rafique, Rochester Institute of Technology
  • Robinson Rivas, UCV
  • Jože Rožanec, Jožef Stefan Institute
  • Seyedehhaleh Seyeddizaji, Klagenfurt University
  • Josef Spillner, Zurich University of Applied Sciences
  • Jacek Sroka, University of Warsaw
  • Dingwen Tao, Indiana University
  • Osamu Tatebe, University of Tsukuba
  • Douglas Thain, University of Notre Dame
  • Rafael Tolosana-Calasanz, Universidad de Zaragoza
  • Massimo Torquati, University of Pisa
  • Aurelio Vivas, Universidad de lo Andes
  • Ahmad Zareie, University of Sheffield
  • Yang Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
  • Jiashu Wu, University of Chinese Academy of Sciences
  • Emilio Serrano, Universidad Politécnica de Madrid

Focus

  • Artificial Intelligence in the IoT-Edge-Cloud continuum
  • Data management in Edge devices and the computing continuum
  • Innovative applications and case studies
  • Large-scale data processing applications in science, engineering, business and healthcare
  • Emerging trends for computing, machine learning, approximate computing, and quantum computing.
  • Parallel, replicated, and highly-available distributed databases
  • Scientific data analytics (Big Data or HPC-based approaches)
  • Middleware for processing large-scale data
  • Programming models for parallel and distributed data analytics
  • Workflow management for data analytics
  • Coupling HPC simulations with in-situ data analysis
  • Parallel data visualization
  • Distributed and parallel transaction, query processing and information retrieval
  • Internet-scale data-intensive applications
  • Sensor network data management
  • Data-intensive computing infrastructures
  • Parallel data streaming and data stream mining
  • New storage hierarchies in distributed data systems
  • Parallel and distributed machine learning, knowledge discovery and data mining
  • Privacy and trust in parallel and distributed data management and analytics systems
  • IoT data management and analytics
  • Parallel and distributed data science applications
  • Data analysis in cloud and serverless models

In cooperation

Organized by

Supported by

Awards support