BigDAW 2021: Big Data Analytics Workshop Virtual Workshop Co-Located with the ACM International Conference on Computing Frontiers 2021 Catania, Italy, May 11-13, 2021 |
Conference website | https://hpc.pnl.gov/bigdaw |
Submission link | https://easychair.org/conferences/?conf=bigdaw2020 |
Abstract registration deadline | February 22, 2021 |
Submission deadline | February 28, 2021 |
Data analytics is transforming the world of science, health, commerce, defense, and social activities. Complex scientific and human systems are being designed, managed, and optimized using first-principles simulations, data science, machine learning, and graph methods. Many real-world analytic workloads are a mix of algorithms and data types best supported by different programming and parallel execution models. The composability of models and the capability of computer systems to efficiently and transparently support the diverse model is key to achieving performance and productivity requirements of emerging real-world uses.
This workshop seeks paper on mixed data analytic workflows, algorithms, composability, optimizations, programming environments, hardware designs, and benchmark studies. Besides regular papers, extended abstracts papers describing innovative ideas related to the workshop theme are also encouraged.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. Authors can submit two types of papers: extended abstracts (2 pages, excluding references) and regular papers (up to 6 pages, including references). All submissions must be single-spaced double-column pages in ACM format.
The templates are available at:
https://www.acm.org/publications/proceedings-template.
List of Topics
Topics of interest, of both theoretical and practical significance, include but are not limited to:
- Applications and workflows integrating scientific simulation, data analytics, and learning
- Libraries, Runtime systems and programming models in support for Big Data Analytics workflows
- Data Structures and Algorithms supporting hybrid data models (e.g., Graphs and Tables and Attributed Graphs).
- Machine Learning and Combinatorial Optimization algorithms
- Approaches for managing massive unstructured datasets (including graph databases and solutions combining learning approaches with graph analytics)
- Explainable AI and Fairness in algorithms
- Innovative algorithmic techniques
- Novel computer architecture design in support of Big Data Analytics workflows: including micro and system level design, accelerators, custom processors and reconfigurable computing.
Organizers
- John Feo, Workshop Co-chair, (PNNL), john.feo@pnnl.gov
- Marco Minutoli, Workshop Co-chair, (PNNL), marco.minutoli@pnnl.gov
- Omer Subashi, Publicity chair, (PNNL), omer.subashi@pnnl.gov
- Sumit Purohit, Virtual Meeting chair, (PNNL), sumit.purohit@pnnl.gov
Program Committee
- Fabrizio Ferrandi, Politecnico di Milano
- Fabrizio Petrini, Intel
- Maurizio Drocco, IBM
- Omer Subashi, Pacific Northwest National Laboratory
- Sumit Purohit, Pacific Northwest National Laboratory
- Ananth Kalyanaraman, Washington State University
- Ivy Peng, Lawrence Livermore National Laboratory
- Alex Fender, NVIDIA
- Kasia Swirydowicz, Pacific Northwest National Laboratory
- Other members TBD
Publication
BigDAW 2020 proceedings will be published in the ACM Digital Library by ACM.
Venue
The virtual workshop will be held in conjunction with the ACM International Conference on Computing Frontiers 2021.
Contact
All questions about submissions should be emailed to the Organizing Committee.