The Sixth IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2022)
Collocated with IEEE BigData 2022
One day in December 17-20, 2022 (Virtual)
Website: https://userpages.umbc.edu/~jianwu/BPOD/
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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.


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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


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Important Dates

Oct 1, 2022: Due date for full workshop papers submission
Nov 1, 2022: Notification of paper acceptance to authors
Nov 20, 2022: Camera-ready of accepted papers
One day in Dec 17-20, 2022: 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/2022/bigdata22/scripts/submit.php?subarea=S20&undisplay_detail=1&wh=/cyberchair/2022/bigdata22/scripts/ws_submit.php


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 2022 Conference (IEEE BigData 2022) which will be published by IEEE Computer Society.

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Workshop Chairs


Zhiyuan Chen, University of Maryland, Baltimore County, U.S.A, zhchen-AT-umbc.edu
Jianwu Wang, University of Maryland, Baltimore County, U.S.A, jianwu-AT-umbc.edu
Feng Chen, University of Texas at Dallas, U.S.A, feng.chen-AT-utdallas.edu
Junqi Yin, Oak Ridge National Laboratory, yinj-AT-ornl.gov


Program Committee

Antonio Badia, University of Louisville, USA
David Bermbach, TU Berlin, Germany
Wanghu Chen, College of Computer Science and Engineering, Northwest Normal University, China
Laurent d'Orazio, Univ Rennes, CNRS, IRISA, France
Tome Eftimov, Jozef Stefan Institute, Slovenia
Yanjie Fu, Missouri University of Science and Technology, United States
Madhusudhan Govindaraju, Binghamton University, USA
Marek Grzegorowski, University of Warsaw, Poland
Xin Guo, Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
Suneuy Kim, San Jose State University, United States
Yunwen Lei, University of Birmingham, UK
Chen Liu, North China University of Technology, China
Soufiana Mekouar, Mohammed V University Rabat, Scientific Institute, Morocco
Baoning Niu, Taiyuan University of Technology, China
Frank Pallas, TU Berlin, Germany
Lauritz Thamsen, Technische Universität Berlin, Germany
Ciprian-Octavian Truică, University Politehnica of Bucharest, Romania
Xiangfeng Wang, East China Normal University, USA
Xiaoming Yuan, Hong Kong University, China
Wenbin Zhang, Carnegie Mellon University, USA |


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Keynote Speakers (TBD)