Third International Workshop on Energy Efficient Scalable Data Mining and Machine Learning
Co-located with ECML PKDD 2020
September 14, 2020 - Ghent, Belgium
This workshop aims to bring together people from different areas and backgrounds in data mining and machine learning that have a common interest in energy efficiency, sustainability, and edge computing.
For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. This has lead to a scenario where most of the machine learning prediction is done in the cloud, incurring in security concerns and increased latency.
However, there is an increasing trend in machine learning which focuses on building models that are able to run in the edge. For instance, Google has released the first speech recognition model running directly on the device, improving latency and reducing energy consumed by the network connectivity. Another example is TensorFlowLite, a powerful tool to deploy models on mobile and IoT devices.
The goal with this workshop is to promote green machine learning even further, by creating a half-day workshop where researchers in different machine learning and data mining areas can bring together their ideas, present them in front of a heterogeneous crowd, and have interesting debates on how to advance machine learning into a more scalable future. This edition includes also applications of machine learning in areas re- lated to environmental data science and climate change, to address the needs of a more sustainable machine learning from a broader perspective.
We accept original work, already completed, or in progress. Position papers are also considered.
Accepted papers will be published in ECML-PKDD 2020 Workshop proceedings.
- Paper submission deadline: Tuesday, June 9, 2020
- Paper acceptance notification: Monday, July 20, 2020
- Paper camera-ready deadline: Monday, August 3, 2020
- Workshop date: Monday, September 14, 2020 (preliminary)
- Eva García-Martín, Ekkono Solutions
- Albert Bifet, Telecom-ParisTech
- Crefeda Faviola Rodrigues, University of Manchester
- Heitor Murilo Gomes, University of Waikato
- Ricardo Baeza-Yates, NTENT and Universitat Pompeu Fabra
- Christian Nordahl, Blekinge Institute of Technology
- Håkan Grahn, Blekinge Institute of Technology
- Rikard König, Ekkono Solutions
- Henrik Linusson, Ekkono Solutions
- Ivar Simonsson, Ekkono Solutions