CALL FOR PAPERS
The Fifth IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2021)
One day in December 15-18, 2021, Virtual
the IEEE Big Data 2021 Conference (IEEE BigData 2021)
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. For example, users have to first choose from many different big data systems and optimization algorithms to deal with complex structured data, graph data, and streaming data. 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.
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.
This workshop is built on top of the successful organization of previous two workshops at the same conference. In both years, our workshop was one of the largest workshop at the conference.
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.
Topics of interests include, but are not limited to:
- Theoretical and empirical performance model 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
- Paper Submission: Oct 29, 2021 (extended)
- Decision Notification: Nov 13, 2021
- Camera-Ready Due Date: Nov 21, 2021
- Workshop Date: One day in Dec 15-18, 2021
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 IEEE Templates for Conference Proceedings). All papers must be submitted via the conference submission system for the workshop.
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 2021 Conference (IEEE BigData 2021) which will be published by IEEE Computer Society.
- 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
- Liqiang Wang, UUniversity of Central Florida, U.S.A, Liqiang.Wang-AT-ucf.edu
Program Committee (To be updated)
- Antonio Badia, University of Louisville, United States
- David Bermbach, TU Berlin, Germany
- Sheriffo Ceesay, University of St Andrews, United Kingdom
- Wanghu Chen, Northwest Normal University, China
- Laurent d'Orazio, Rennes University, France
- Tome Eftimov, Jožef Stefan Institute, Slovenia
- Yanjie Fu, University of Central Florida, United States
- Madhusudhan Govindaraju, Binghamton University, United States
- Marek Grzegorowski, University of Warsaw, Poland
- Xin Guo, The Hong Kong Polytechnic University, Hong Kong
- Suneuy Kim, San Jose State University, United States
- Yunwen Lei, University of Birmingham, United Kingdom
- Chen Liu, North China University of Technology, China
- Soufiana Mekouar, Mohammed V University Rabat, 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
- Puyu Wang, Northwest University, China
- Xiangfeng Wang, East China Normal University, China
- Yangyang Xu, Rensselaer Polytechnic Institute, United States
- Xiaoming Yuan, Hong Kong University, China
- Wenbin Zhang, Carnegie Mellon University, United States
- Chen Zhao, Kitware Company, United States
- Geoffrey Fox, Indiana University
- Le Gruenwald, University of Oklahoma
- Dhabaleswar K. (DK) Panda, Ohio State University
- Jianfeng Zhan, Chinese Academy of Sciences
Jianwu Wang's homepage.
IEEE BigData 2021.