CALL FOR PAPERS

The Third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019)

http://userpages.umbc.edu/~jianwu/BPOD/
December 11, 2019, Los Angeles, CA, USA
at the IEEE Big Data 2019 Conference (IEEE BigData 2019)

BPOD 2019 will be held on Wednesday, December 11. The tentative program of BPOD 2019 can be found at http://userpages.umbc.edu/~jianwu/BPOD/bpod-2019-schedule.pdf

Description

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.

• In 2017, 20 papers were accepted and presented at the workshop. Its program and presentations are at http://userpages.umbc.edu/~jianwu/BPOD/BPOD-2017-Papers.html.
• In 2018, 17 papers were accepted and presented at the workshop. Its program and presentations are at http://userpages.umbc.edu/~jianwu/BPOD/BPOD-2018-Papers.html.

Special Issue

We are organizing a special issue on the same topic at the Big Data Research, a leading journal on big data at Elsevier. Selected papers from the workshop will be invited to submit an extended version to the special issue. We also welcome manuscript sumbmissions to the special issue directly via open solicitation. Submission deadline is April 1st, 2020. More information is at https://www.journals.elsevier.com/big-data-research/call-for-papers/benchmarking-performance-tuning-and-optimization.

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

Important Dates

• Paper Submission: Oct 21, 2019 (extended)
• Decision Notification: Nov 12, 2019
• Camera-Ready Due Date: Nov 20, 2019

Paper Submission (To be updated)

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 2019 Conference (IEEE BigData 2019) 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, Northwest Normal University
• Laurent d'Orazio, Rennes University
• Yanjie Fu, Missouri University of Science and Technology
• Madhusudhan Govindaraju, Binghamton University
• Xin Guo, Hong Kong Polytechnic University
• Ting Hu, Wuhan University
• Zhe Jiang, University of Alabama
• Min Li, IBM Research - Almaden
• 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
• Xiangfeng Wang, East China Normal University
• Qiang Wu, Middle Tennessee State University
• Yangyang Xu, Rensselaer Polytechnic Institute
• Baijian Yang, Purdue University
• Xiaoming Yuan, Hong Kong University
• Jin Zhang, AMD
• Liang Zhao, George Mason University
• Xun Zhou, The University of Iowa

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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