The Second IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2018)

December 10-13, 2018, Seattle, WA, USA

Call For Paper

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.

This workshop is built on top of the successful organization of last year's workshop at the same conference. 20 papers were accepted and presented at the workshop, which makes it one of the largest workshop at the conference in 2017.

Topics of interest include, but are not limited to, the following topic areas: :

• 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

Submission Deadline	:	October 22, 2018
Notification of acceptance:	:	November 13, 2018
Camera-ready submission	:	November 20, 2018



User Name : alex
Posted 17-10-2018 on 17:29:04 AEDT

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