BPOD 2024

CALL FOR PAPERS

The Seventh IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2024)

Sunday, December 15, Washington DC, USA, 2024
at IEEE Big Data 2024 Conference (IEEE BigData 2024)

Description

Users of big data are often not computer scientists. On the other hand, it is nontrivial for even experts to optimize the 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 the 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 the quality of results, monetary cost, security and privacy, and energy efficiency. In more traditional algorithms and relational databases, these complexities are handled by query optimizers and other automatic tuning tools (e.g., index selection tools) and there are benchmarks to compare the performance of different products and optimization algorithms. Such tools are not available for big data environments 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’s agenda can be found at bpod-2024-agenda.

This workshop is built on top of the successful organization of previous workshops at the same conference. In previous years, our workshop was one of the largest workshops at the conference.

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 29, 2024 (Extended)
  • Decision Notification: Nov 13, 2024
  • Camera-Ready Due Date: Nov 23, 2024
  • Workshop Date: One day in Dec 15-18, 2024

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 IEEE Templates for Conference Proceedings). All papers must be submitted via the conference submission system for the workshop.

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 2022 Conference (IEEE BigData 2022) 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
  • Feng Chen, University of Texas at Dallas, U.S.A, feng.chen-AT-utdallas.edu
  • Junqi Yin, Oak Ridge National Laboratory, U.S.A, yinj-AT-ornl.gov

Program Committee

  • Sahara Ali, University of North Texas, United States
  • Antonio Badia, University of Louisville, United States
  • Laurent D’Orazio, Rennes University, France
  • Tome Eftimov, Jožef Stefan Institute, Slovenia
  • Yanjie Fu, Arizona State University, United States
  • Madhusudhan Govindaraju, Binghamton University, United States
  • Marek Grzegorowski, University of Warsaw, Poland
  • Xin Huang, Towson University, United States
  • Shad Kirmani, LinkedIn Corp., United States
  • Soufiana Mekouar, Mohammed V University Rabat, Morocco
  • Baoning Niu, Taiyuan University of Technology, China
  • Mijanur Palash, Oak Ridge National Laboratory, United States
  • Frank Pallas, Paris Lodron University of Salzburg, Austria
  • Lauritz Thamsen, University of Glasgow, United Kingdom
  • Xiangfeng Wang, East China Normal University, China