The Seventh IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2024)
Collocated with IEEE BigData 2024
One day in Dec 15-18, 2024: Washington DC, USA
Website: https://bdal.umbc.edu/bpod-2024/
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Users of big data are often not computer scientists. On the other hand, it is nontrivial for even experts to optimize performance of big data applications because there are so many decisions to make. In particular, there are numerous parameters to tune to optimize performance of a specific system and it is often possible to further optimize the algorithms previously written for “small” data in order to effectively adapt them in a big data environment. To make things more complex, users may worry about not only computational running time, storage cost and response time or throughput, but also quality of results, monetary cost, security and privacy, and energy efficiency. In more traditional algorithms and relational databases, these complexities are handled by query optimizer and other automatic tuning tools (e.g., index selection tools) and there are benchmarks to compare performance of different products and optimization algorithms. Such tools are not available for big data environment and the problem is more complicated than the problem for traditional relational databases.

Research Topics:
The aim of this workshop is to bring researchers and practitioners together to better understand the problems of optimization and performance tuning in a big data environment, to propose new approaches to address such problems, and to develop related benchmarks, tools and best practices. Topics of interest include, but not limited to:

- Theoretical and empirical performance models for big data applications
- Optimization for Machine Learning and Data Mining in big data
- Benchmark and comparative studies for big data processing and analytic platforms
- Monitoring, analysis, and visualization of performance in big data environment
- Workflow/process management & optimization in big data environment
- Performance tuning and optimization for specific big data platforms or applications (e.g., No-SQL databases, graph processing systems, stream systems, SQL-on-Hadoop databases)
- Performance tuning and optimization for specific data sets (e.g., scientific data, spatio data, temporal data, text data, images, videos, mixed datasets)
- Case studies and best practices for performance tuning for big data
- Cost model and performance prediction in big data environment
- Impact of security/privacy settings on performance of big data systems
- Self adaptive or automatic tuning tools for big data applications
- Big data application optimization on High Performance Computing (HPC) and Cloud environments

This workshop is built on top of the successful organization of the last six workshops at the same conference. In all six years we received well over 20 submissions and around 16 papers were accepted and presented each year, which makes it one of the largest workshops at the conference. We skipped last year just because of the availability of organizers for international travel.

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Important Dates:
Oct 1, 2024: Due date for full workshop papers submission
Nov 4, 2024: Notification of paper acceptance to authors
Nov 20, 2024: Camera-ready of accepted papers
One day in Dec 15-18, 2024: Workshop


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Paper Submission


Authors are invited to submit full papers (maximal 10 pages) or short papers (maximal 6 pages) with references included in the IEEE 2-column format.
Templates for LaTex, Word and PDF can be found at
(https://www.ieee.org/conferences/publishing/templates.html).

You are strongly encouraged to print and double check your PDF file before its submission, especially if your paper contains Asian/European language symbols (such as Chinese/Korean characters or English letters with European fonts).

All papers must be submitted via the conference submission system for the workshop at:
https://wi-lab.com/cyberchair/2024/bigdata24/scripts/submit.php?subarea=S13


At least one author of each accepted paper is required to attend the workshop virtually and present the paper. All the accepted papers by the workshops will be included in the Proceedings of the IEEE Big Data 2024 Conference (IEEE BigData 2024) which will be published by IEEE Computer Society.

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Workshop Chairs

Program Chairs:
Zhiyuan Chen, University of Maryland Baltimore County
Jianwu Wang, University of Maryland Baltimore County
Feng Chen, University of Texas at Dallas
Junqi Yin, Oak Ridge National Laboratory

Confirmed PC members (More to be invited)

Antonio Badia, University of Louisville
Laurent d'Orazio, Rennes University
Yanjie Fu, University of Central Florida
Madhusudhan Govindaraju, Binghamton University
Marek Grzegorowski, University of Warsaw
Soufiana Mekouar, Mohammed V University Rabat, Scientific Institute
Lauritz Thamsen,University of Glasgow
Xiangfeng Wang, East China Normal University



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Keynote Speakers (TBD)