[AISWorld] CPF: Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications at IEEE BigData 2017

Jianwu Wang jianwu at umbc.edu
Sun Jul 9 16:47:21 EDT 2017


CALL FOR PAPERS 

The IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2017)

http://userpages.umbc.edu/~jianwu/BPOD-2017/ <http://userpages.umbc.edu/~jianwu/BPOD-2017/>
one day during December 11-14, 2017, Boston, MA, USA 
at the IEEE Big Data 2017 Conference (IEEE BigData 2017) <http://cci.drexel.edu/bigdata/bigdata2017/>
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 such as those dealing with structured data (e.g., Apache Hbase, Mongo DB, Apache Hive, Apache Accumulo, Presto, Spark SQL), graph data (e.g., Pregel, Giraph, GraphX, GraphLab), and streaming data (e.g., Apache Storm, Apache Heron, Apache Flink, Samza). In addition, there are numerous parameters to tune to optimize performance of a specific system. To make things more complex, users may worry about not only response time or throughput, but also quality of results, monetary cost, security and privacy, and energy efficiency. In more traditional 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. Such tools are not available for big data environment and the problem is probably 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
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 10, 2017
Decision Notification: Nov 1, 2017
Camera-Ready Copy Due Date: Nov 15, 2017
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 (download Word templates <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.doc>, download PDF templates <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.pdf> or LaTeX templates <ftp://pubftp.computer.org/Press/Outgoing/proceedings/IEEE_CS_Latex8.5x11x2.zip>). All papers must be submitted via the conference submission system for the workshop <https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submit.php?subarea=S10&undisplay_detail=1&wh=/cyberchair/2017/bigdata17/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 2017 Conference (IEEE BigData 2017) 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
Program Committee (To be updated)

Ilkay Altintas, University of California San Diego
David Bermbach, TU Berlin
Chritian Konig, Microsoft Research
Shiyong Lu, Wayne State University
Frank Pallas, TU Berlin
Madhusudhan Govindaraju, Binghamton University
Min Li, IBM TJ Waston Research Center
Xiaoyi Lu, Ohio State University
Keynote Speakers

Geoffrey Fox, Indiana University
Steering Committee (To be updated)

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

Sincerely yours

Jianwu Wang, Ph.D.
jianwu at umbc.edu
http://userpages.umbc.edu/~jianwu/

Assistant Professor in Big Data
Department of Information Systems
University of Maryland, Baltimore County
410-455-3883 




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