Knowledge & Data Engineering Systems
The Knowledge & Data Engineering Systems (KDES) research group is part of the Information, Data and Analysis (IDA) Section. KDES brings together the fundamental research areas of Distributed Data Systems, Data Engineering, and Data Science.
KDES's strength lies in the spectrum of theoretical backgrounds and applications ranging from large-scale Distributed Computing Systems and Information Systems, to Edge Computing and Distributed Machine Learning/AI, and focuses on building innovative distributed data science and engineering systems.
News & Events
06 SepOur paper on ' FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization' has been accepted for presentation at the CORE A* IEEE International Conference on Data Mining (IEEE ICDM 2023 [https://www.cloud-conf.net/icdm2023/]), December 1-4, 2023, Shanghai, China (partially funded by EU Horizon TRACE Grant [https://trace-horizon.eu/]).
27 JunKian Wang gives a talk on 'Maintenance of Model Resilience in Distributed Edge Learning Environments' at the IEEE Intelligent Environments (IE 2023) Conference, Jun 27, 2023 - Jun 30, 2023, Mauritius.
14 Jun@TRACE_HORIZON had the KoM (12-13/06) in Athens at @dit_uoa. 28 partners from 11 countries will work the next 3 years to offer a universal platform with functionalities related to planning, scheduling, optimization and events management for the logistics sector.
02 MayThe MAB-KD 2023 Workshop, in conjunction with the 23rd IEEE ICDM 2023 Conference, Dec 4, 2023, Shanghai, China, aims to provide a forum for disseminating late-breaking research ideas, paradigms, and results related to the adoption and current developments of MAB systems in knowledge discovery and data mining in a high variety of application domains, bringing together researchers from academia and industry.
02 May2nd IEEE EdgeAI-IoT 2023 Workshop in conjunction with IEEE 9th World Forum on Internet of Things 12–27 October 2023, Aveiro, Portugal