Chairs
- Erhard Rahm, Leipzig University, Germany
- Jeyan Thiyagalingam, Rutherford Appleton Laboratory, UK
Program Committee
- Ashiq Anjum, University of Leicester, UK
- Achim Basermann, German Aerospace Center (DLR), Simulation and Software Technology
- Matthias Boehm, TU Berlin
- José M Cecilia, Universitat Politècnica de València
- Alexandru Costan, INRIA
- Hao Dai, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
- Reza Farahani, University of Klagenfurt
- Rafael Ferreira da Silva, Oak Ridge National Laboratory
- Sukhpal Singh Gill, Queen Mary University of London
- Ligang He, The University of Warwick
- Shadi Ibrahim, Inria, Rennes Bretagne Atlantique Research Center
- Odej Kao, TU Berlin
- Hideyuki Kawashima, Keio University
- Youngjae Kim, Sogang University
- Dalibor Klusacek, CESNET, Brno, Czech Republic
- Michael Kuhn, Otto von Guericke University Magdeburg
- 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
- M. Mustafa Rafique, Rochester Institute of Technology
- Jože M. Rožanec, Jožef Stefan Institute
- Rizos Sakellariou, The University of Manchester
- Josef Spillner, Zurich University of Applied Sciences
- Osamu Tatebe, University of Tsukuba
- Douglas Thain, University of Notre Dame
- Rafael Tolosana-Calasanz, Universidad de Zaragoza
- Massimo Torquati, University of Pisa
- Feiyi Wang, Oak Ridge National Laboratory
- Yang Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
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
