[DBWorld] IEEE BPOD 2021 workshop collocated with IEEE BigData 2021

=EF=BB=BFThe Fifth IEEE International Workshop on Benchmarking, Performance=
 Tuning and Optimization for Big Data Applications (BPOD 2021)
Collocated with IEEE BigData 2021
One day in December 15-18, 2021 (Virtual)
Website: https://userpages.umbc.edu/~jianwu/BPOD/
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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 applicat=
ions because there are so many decisions to make. In particular, there are =
numerous parameters to tune to optimize performance of a specific system an=
d it is often possible to further optimize the algorithms previously writte=
n for =E2=80=9Csmall=E2=80=9D data in order to effectively adapt them in a =
big data environment. To make things more complex, users may worry about no=
t only computational running time, storage cost and response time or throug=
hput, 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 tun=
ing 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 envi
 ronment and the problem is more complicated than the problem for tradition=
al relational databases.


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Research Topics:
The aim of this workshop is to bring researchers and practitioners together=
 to better understand the problems of optimization and performance tuning i=
n a big data environment, to propose new approaches to address such problem=
s, and to develop related benchmarks, tools and best practices. Topics of i=
nterest include, but not limited to:


* Theoretical and empirical performance models for big data applications
* Optimization for Machine Learning and Data Mining in big data
* Benchmark and comparative studies for big data processing and analytic pl=
atforms
* Monitoring, analysis, and visualization of performance in big data enviro=
nment
* Workflow/process management & optimization in big data environment
* Performance tuning and optimization for specific big data platforms or ap=
plications (e.g., No-SQL databases, graph processing systems, stream system=
s, SQL-on-Hadoop databases)
* Performance tuning and optimization for specific data sets (e.g., scienti=
fic data, spatio data, temporal data, text data, images, videos, mixed data=
sets)
* 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






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Important Dates




Oct 1, 2021: Due date for full workshop papers submission
Nov 1, 2021: Notification of paper acceptance to authors
Nov 20, 2021: Camera-ready of accepted papers
One day in Dec 15-18, 2021: Workshop




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Paper Submission




Authors are invited to submit full papers (maximal 10 pages) or short paper=
s (maximal 6 pages) as per IEEE 8.5 x 11 manuscript guidelines. Templates f=
or 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 w=
orkshop at (please select #17 in the list):
https://wi-lab.com/cyberchair/2021/bigdata21/scripts/submit.php?subarea=3DS=
17




At least one author of each accepted paper is required to attend the worksh=
op 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 BigD=
ata 2021) which will be published by IEEE Computer Society.




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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, j=
ianwu-AT-umbc.edu
            Feng Chen, University of Texas at Dallas, U.S.A, feng.chen-AT-u=
tdallas.edu
            Liqiang Wang, University of Central Florida, U.S.A., Liqiang.Wa=
ng-AT-ucf.edu






Program Committee (To be updated)




Antonio Badia,   University of Louisville  =

David Bermbach,   TU Berlin
Sheriffo Ceesay,   University of St Andrews  =

Wanghu Chen,   College of Computer Science and Engineering, Northwest Norma=
l University  =

Laurent d'Orazio ,  Rennes University  =

Yanjie Fu,   Missouri University of Science and Technology  =

Madhusudhan Govindaraju,   Binghamton University  =

Xin Guo,   Department of Applied Mathematics, The Hong Kong Polytechnic Uni=
versity  =

Suneuy Kim,   San Jose State University  =

Yunwen Lei,   University of Birmingham  =

Chen Liu,   North China University of Technology  =

Frank Pallas,   TU Berlin  =

Lauritz Thamsen,   Technische Universit=C3=A4t Berlin  =

Puyu Wang,   Northwest University (China)  =

Xiangfeng Wang,   East China Normal University  =

Yangyang Xu,   Rensselaer Polytechnic Institute  =

Xiaoming Yuan,   Hong Kong University =









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Keynote Speakers (TBD)
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