[hpc-announce] CFP: DRBSD-9: The 9th International Workshop on Data Analysis and Reduction for Big Scientific Data (with SC23)

[Tao, Dingwen]

The 9th International Workshop on Data Analysis Reduction for Big Scientific Data (DRBSD-9) 
in Conjunction with SC’23, November 13, 2023, Denver, CO
 
As the speed gap between compute and storage continues to exist and widen, the increasing data volume and velocity pose major challenges for big data applications. This demands new research and software tools that can further reduce data by several orders of magnitude, taking advantage of new architectures and hardware available on next generation systems. This international workshop on data analysis & reduction is a response to this renewed research direction and will provide a focused venue for researchers to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community. Topics of interest include but are not limited to:
• Data reduction methods for scientific data including:
o       Data deduplication methods
o       Motif-specific methods (structured and unstructured meshes, particles, tensors, …)
o       Methods with accuracy guarantees
o       Feature/QoI-preserving reduction
o       Optimal design of data reduction methods
o       Compressed sensing and singular value decomposition
• Metrics to assess reduction quality and provide feedback 
• Data analysis over extreme-scale datasets
o       AI/ML methods
o       Surrogate/reduced-order models
o       Feature extraction
o       Visualization techniques
o       Artifact removal during reconstruction
o       Methods that take advantage of the reduced data
• Data analysis and reduction co-design 
o       Methods for using accelerators
o       Accuracy and performance trade-offs on current and emerging hardware
o       New programming models for managing reduced data
o       Runtime systems for data reduction
• Large-scale code coupling and workflows
• Experience of applying data reduction and analysis in practical applications or use-cases
o       State of the practice
o       Application use-cases which can drive the community to develop MiniApps

Submissions
Papers should be submitted electronically on the SC submission system
(https://submissions.supercomputing.org/)
• Paper submission must be in IEEE format. 
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
• DRBSD-9 will accept full papers (limited to 8 pages excluding references) and extended abstracts (2 pages, except references and appendix).
• DRBSD-9 encourages submissions to provide artifact description & evaluation.  Details: 
https://sc23.supercomputing.org/submit/reproducibility-initiative/
• Detailed submission URL will be provided once the workshop is accepted by SC.
Submitted papers will be evaluated by at least 3 reviewers based upon technical merits and the accepted paper will be published with IEEE/ACM through IEEE TCHPC.

Tentative Planned Timeline
• Selection of program committee members: June 3, 2023
• Paper deadline: August 26, 2023 (AoE)
• Author notification: September 13, 2023
• Camera ready final papers: September 23, 2023 (AoE)
• Selection of invited talks and panelists: September 30, 2023
 
Expected Outcome
The goal of this workshop is to provide a focused venue for researchers in all aspects of data reduction and analysis to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community. 

Tentative Technical Program Committee
Dingwen Tao, Indiana University (chair)
Mark Ainsworth, Brown University
Allison Baker, NCAR
Martin Burtscher, Texas State University
Michael Bussmann, Helmholtz-Zentrum Dresden-Rossendorf
Frank Cappello, Argonne National Laboratory
Sheng Di, Argonne National Laboratory
Ian Foster, Argonne National Laboratory/University of Chicago
Dorit M. Hammerling, Colorado School of Mine
Xubin He, Temple University
Dan Huang, Sun Yat-sen University
Scott Klasky, Oak Ridge National Laboratory
Kerstin Kleese van Dam, Brookhaven National Laboratory
Xin Liang, Missouri University of Science and Technology
Qing Liu, New Jersey Institute of Technology
Peter Lindstrom, Lawrence Livermore National Laboratory
Todd Munson, Argonne National Laboratory
Michela Taufer, University of Tennessee
Andreas Wicenec, University of Western Australia
John Wu, Lawrence Berkeley National Laboratory
Jieyang Chen, Oak Ridge National Laboratory
Wen Xia, https://cswxia.github.io/, Harbin Institute of Technology, China

Dingwen Tao
On behalf of DRBSD-9 Organizing Committee