The Fourth IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2020)
Collocated with IEEE BigData 2020
One day in December 10-13, 2020 (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.


Please note this year's workshop will be held virtually because the collocated main conference is moving to virtual conference. Proceedings of the workshop will be published as planned. We will provide details on how to attend this workshop virtually when it is approaching. 


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




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Important Dates




        Paper Submission: Oct 1, 2020
        Decision Notification: Nov 1, 2020
        Camera-Ready Due Date: Nov 25, 2020




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Paper Submission




Authors are invited to submit full papers (maximal 10 pages) or short papers (maximal 6 pages) as per IEEE 8.5 x 11 manuscript guidelines. Templates for LaTex, Word and PDF can be found at
https://www.ieee.org/conferences/publishing/templates.html




All papers must be submitted via the conference submission system for the workshop at (please select #17 in the list):
https://wi-lab.com/cyberchair/2020/bigdata20/scripts/ws_submit.php


At least one author of each accepted paper is required to attend the workshop and present the paper. All the accepted papers by the workshops will be included in the Proceedings of the IEEE Big Data 2020 Conference (IEEE BigData 2020) which will be published by IEEE Computer Society.




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
        Yiming Ying, University at Albany-SUNY, U.S.A, yying-AT-albany.edu




Program Committee (To be updated)


 David Bermbach, TU Berlin
 Wanghu Chen,College of Computer Science and Engineering, Northwest Normal University
 Laurent d'Orazio, Rennes University
 Yanjie Fu,  Missouri University of Science and Technology
 Shahram Ghandeharizadeh, University of Southern California
 Madhusudhan Govindaraju, Binghamton University
 Xin Guo, The Hong Kong Polytechnic University
 Ting Hu, Wuhan University
 Zhe Jiang, University of Alabama
 Yunwen Lei, University of Kaiserslautern
 Chen Liu, North China University of Technology
 Shiyong Lu, Wayne State University
 Xiaoyi Lu, The Ohio State University
 Frank Pallas, TU Berlin
 Rong Shi, Facebook
 Puyu wang, Northwest University (China)
 Xiangfeng Wang, East China Normal University
 Qiang Wu, Middle Tennessee State University
 Yangyang Xu, Rensselaer Polytechnic Institute
 Xiaoming Yuan, Hong Kong University
 Jing Zhang, AMD


Steering Committee


        Geoffrey Fox, Indiana University
        Le Gruenwald, University of Oklahoma
        Dhabaleswar K. (DK) Panda, Ohio State University
        Jianfeng Zhan, Chinese Academy of Sciences


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Keynote Speakers (TBD)