[hpc-announce] CfP: ACM DEBS 2022 Industrial and Application Track

Valeria Cardellini cardellini at ing.uniroma2.it
Thu Mar 3 10:46:20 CST 2022

2022 ACM International Conference on Distributed and Event‐Based Systems (DEBS 2022) 

June 27-July 1, 2022 - Copenhagen, Denmark

Website: https://2022.debs.org/ <https://2022.debs.org/>
Submission: https://cmt3.research.microsoft.com/DEBS2022 <https://cmt3.research.microsoft.com/DEBS2022>

The DEBS 2022 Industry and Application Track invites submissions on innovative design, development, or deployments of event-based and distributed systems and applications. Contributions will be reviewed by researchers and industry practitioners working in distributed and event-based computing.

The CfP is available at: https://2022.debs.org/call-for-industry-papers/ <https://2022.debs.org/call-for-industry-papers/> 

--- Important Dates ---
Paper submission: March 25th, 2022
paper notification: April 22nd, 2022
Camera ready: May 6th, 2022
Conference June 27th-July 1st, 2022

--- Topics ---
Submissions should present novel work and experiences with relevant topics including, but not limited to, the following:
Use cases and applications of distributed and event-based systems, also in emerging domains (e.g., personalized health, digital twins)
Models, architectures and paradigms of distributed and event-based systems
Cloud- and edge-based approaches, including serverless, for event-based and distributed applications
Distributed systems trade-offs for event-based applications
Experiences with load management, fault tolerance and reconfiguration of event-based and distributed systems
Transactional support for distributed and event-based systems
Security issues for distributed and event-based systems
Data management in distributed and event-based systems
Programming languages and DSLs for distributed and event-based systems
Deploying and operating distributed and event-based systems and applications
Testing and benchmarking of real-world, distributed and event-based systems
Event processing for training machine learning models
Inferencing of machine learning models from event streams
Experiences with stream processing on heterogeneous and reconfigurable hardware

--- Track Co-Chairs ---
Valeria Cardellini, University of Rome Tor Vergata
Yingjun Wu, Singularity Data

More information about the hpc-announce mailing list