-------- Forwarded Message --------
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/benchmarking-performance-tuning-and-optimization
=============================================
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)
--
Best wishes
Sincerely yours
Jianwu Wang, Ph.D.
jianwu@umbc.edu
http://userpages.umbc.edu/~jianwu/
Twitter: https://twitter.com/jianwuwang
Assistant Professor of Data Science
Department of Information Systems
CyberTraining: Big Data + HPC + Atmospheric Sciences (cybertraining.umbc.edu <http://cybertraining.umbc.edu/>)
University of Maryland, Baltimore County
410-455-3883
ITE 423
_______________________________________________
AISWorld mailing list
AISWorld@lists.aisnet.org