Escrita de artigo para BDR Special issue on Benchmarking, Performance Tuning and Optimization for Big Data Analytics
Desde o dia 20/05 estou participando a elaboração de um artigo para essa revista Big Data Research (BDR) com pesquisadores de uma universidade de Aveiro - Portugal.
O foco da escrita no momento está em técnicas de tuning e self-tuning em banco de dados tradicionais e distribuídos, incluindo os NoSQL.
Abaixo a chamada para o artigo e o prazo foi estendido para 08/06/2020.
Big Data Research Special Issue on "Benchmarking, Performance Tuning and Optimization for Big Data Analytics"
https://www.journals.elsevier.
Scope and Aims
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.
Topics of Interest
We invite authors from academia and industry to submit their original research as well as review articles to present latest progresses for current development or future goals in this field.
Topics of interest 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, spatial 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
Special Issue Editors
* Zhiyuan Chen, University of Maryland, Baltimore County, U.S.A, zhchen@umbc.edu, https://userpages.umbc.edu/~
* Jianwu Wang, University of Maryland, Baltimore County, jianwu@umbc.edu, http://userpages.umbc.edu/~
* Yiming Ying, University at Albany-SUNY, U.S.A, yying@albany.edu, https://www.albany.edu/~
* Feng Chen, University of Texas at Dallas, U.S.A, feng.chen@utdallas.edu
Important Dates
Submission Deadline: June, 1st, 2020
Author Notification: July, 15th, 2020
Revised Manuscript Due: September, 1st, 2020
Notification of Acceptance: October, 1st, 2020
Final Manuscript Due: November, 1st, 2020
Tentative Publication Date: December, 1st, 2020
About the journal:
Big Data Research is a prestigious journal in the area of big data. It has an impact factor of 2.95. More details can be found at https://www.journals.elsevier.
Paper Submission Guidelines
This issue will contain papers directly submitted from the wider research community along with selected papers from the called this year and past years' IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD, https://userpages.umbc.edu/~
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for the journal. https://www.elsevier.com/
The complete manuscript should be submitted through the journal's submission system. https://www.editorialmanager.
To ensure that you submit to the correct special issue, please select the appropriate section in the dropdown menu upon submission and choose by Article Type: VSI:BigDataBench&Optimizatio. In your cover letter, please also clearly mention the title of the SI.
O foco da escrita no momento está em técnicas de tuning e self-tuning em banco de dados tradicionais e distribuídos, incluindo os NoSQL.
Abaixo a chamada para o artigo e o prazo foi estendido para 08/06/2020.
Big Data Research Special Issue on "Benchmarking, Performance Tuning and Optimization for Big Data Analytics"
https://www.journals.elsevier.
Scope and Aims
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.
Topics of Interest
We invite authors from academia and industry to submit their original research as well as review articles to present latest progresses for current development or future goals in this field.
Topics of interest 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, spatial 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
Special Issue Editors
* Zhiyuan Chen, University of Maryland, Baltimore County, U.S.A, zhchen@umbc.edu, https://userpages.umbc.edu/~
* Jianwu Wang, University of Maryland, Baltimore County, jianwu@umbc.edu, http://userpages.umbc.edu/~
* Yiming Ying, University at Albany-SUNY, U.S.A, yying@albany.edu, https://www.albany.edu/~
* Feng Chen, University of Texas at Dallas, U.S.A, feng.chen@utdallas.edu
Important Dates
Submission Deadline: June, 1st, 2020
Author Notification: July, 15th, 2020
Revised Manuscript Due: September, 1st, 2020
Notification of Acceptance: October, 1st, 2020
Final Manuscript Due: November, 1st, 2020
Tentative Publication Date: December, 1st, 2020
About the journal:
Big Data Research is a prestigious journal in the area of big data. It has an impact factor of 2.95. More details can be found at https://www.journals.elsevier.
Paper Submission Guidelines
This issue will contain papers directly submitted from the wider research community along with selected papers from the called this year and past years' IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD, https://userpages.umbc.edu/~
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for the journal. https://www.elsevier.com/
The complete manuscript should be submitted through the journal's submission system. https://www.editorialmanager.
To ensure that you submit to the correct special issue, please select the appropriate section in the dropdown menu upon submission and choose by Article Type: VSI:BigDataBench&Optimizatio. In your cover letter, please also clearly mention the title of the SI.
O artigo foi submetido em 08/06/2020 e em 13/09/2020 obtivemos o retorno que o artigo deveria ser revisado para publicação.
ResponderExcluirEm 13/10/2020 a nova versão foi submetida e estamos aguardando a resposta final.
Artigo aceito e publicado
Excluir