SEAA 2021

Call for Papers

Special Session @ 47th EUROMICRO SEAA Conference in Palermo / Italy

Data and AI driven engineering

September 1-3, 2021

https://dsd-seaa2021.unipv.it/seaa/index.html

Motivation: Over the last decade, the prominence of artificial intelligence (AI) and specifically machine- and deep-learning (ML/DL) solutions has grown exponentially. Because of the big data era, and with companies collecting customer and product data from an increasing number of connected devices, more data is available than ever before and can be used for training ML/DL solutions. In parallel, the progress in high-performance parallel hardware such as GPUs and FPGAs allows for training solutions of scales unfathomable even a decade ago. These two concurrent technology developments are at the heart of the rapid adoption of ML/DL solutions in industry.

The hype around AI has resulted in virtually every company has some form of AI initiative, or host of AI initiatives, ongoing and the number of experiments and prototypes in industry is phenomenal. However, research shows that the transition from prototype to industry-strength, production-quality deployment of ML/DL models proves to be challenging for many companies. The engineering challenges, and the related data management challenges, prove to be significant even if many data scientists and companies fail to recognize these.

The track is an integral part of the 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021.

Topics of Interest include, but are not restricted to:

• Solutions to assess and guarantee data quality for ML
• Design methods and approached for ML/DL models
• Distributed ML/DL models in embedded systems
• Automated labelling of data for ML
• Adoption of DataOps, DataOps and/or MLOps practices in large-scale software engineering
• Engineering aspects of training, transfer learning and reinforcement learning
• Engineering effective ML/DL deployments
• Management of data pipelines for ML/DL
• Automated experimentation and Autonomously improving systems
• Feature experimentation and data driven development practices (e.g., A/B testing)
• Federated learning and Distributed AI
• Reinforcement learning and Multi-armed bandits

In particular, we encourage submissions demonstrating the benefits and/or challenges with regards to the development, deployment and evolution of the technologies mentioned above - as well as the adoption and application of the practices, tools and techniques related to these. We welcome submissions providing empirical case study data to illustrate how companies approach this shift in development paradigms.

Special Session Organizers:
Helena Holmström Olsson, helena.holmstrom.olsson@mau.se, Malmö University, Sweden
Jan Bosch, jan.bosch@chalmers.se, Chalmers University of Technology, Sweden

Program Committee:
Philipp Haindl, Johannes Kepler University Linz, Institute of Business Informatics - Software Engineering
Michael Felderer, University of Innsbruck
Christoph Elsner, Siemens AG
Antonio Martini, University of Oslo
Xiaofeng Wang, Free University of Bozen-Bolzano
Stefan Wagner, University of Stuttgart
Aleksander Fabijan, Microsoft
Matthias Tichy, Ulm University
Eric Knauss Chalmers, University of Gothenburg
Patricia Lago, VU University Amsterdam
Tommi Mikkonen, University of Helsinki
Iris Figalist, Siemens AG
Ilias Gerostathopoulos, Vrije Universiteit Amsterdam