-------- Forwarded Message -------- Subject: [AISWorld] CFP: The Third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019) Date: Fri, 6 Sep 2019 07:43:53 -0400 From: Jianwu Wang jianwu@umbc.edu To: aisworld@lists.aisnet.org

The Third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019) Collocated with IEEE BigData 2019, special issue with BDR (Big Data Research) One day during December 9-12, 2019, Los Angeles, CA, USA Website: https://userpages.umbc.edu/~jianwu/BPOD/ =============================================

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.

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 1, 2019 Decision Notification: Nov 1, 2019 Camera-Ready Due Date: Nov 15, 2019

========================================= 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: https://wi-lab.com/cyberchair/2019/bigdata19/scripts/submit.php?subarea=S08

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.

=========================================== Special Issue

Selected accepted papers will be invited to submit to a special issue of Big Data Research (BDR), a leading journal on big data. Detail information can be found at:

https://www.journals.elsevier.com/big-data-research/call-for-papers/benchmar...

============================================= 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 at Albany-SUNY, U.S.A, fchen5-AT-albany.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

===================== Keynote Speakers (TBD)