BPOD 2024


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

One day in December 15-18, Washington DC, USA, 2024
at IEEE Big Data 2024 Conference (IEEE BigData 2024)


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 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 1, 2024
  • Decision Notification: Nov 4, 2024
  • Camera-Ready Due Date: Nov 20, 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 (To be updated)

  • Antonio Badia, University of Louisville, United States
  • David Bermbach, TU Berlin, Germany
  • Sheriffo Ceesay, University of St Andrews, United Kingdom
  • Wanghu Chen, Northwest Normal University, China
  • Laurent d’Orazio, Rennes University, France
  • Tome Eftimov, Jožef Stefan Institute, Slovenia
  • Yanjie Fu, University of Central Florida, United States
  • Madhusudhan Govindaraju, Binghamton University, United States
  • Marek Grzegorowski, University of Warsaw, Poland
  • Xin Guo, The Hong Kong Polytechnic University, Hong Kong
  • Suneuy Kim, San Jose State University, United States
  • Yunwen Lei, University of Birmingham, United Kingdom
  • Chen Liu, North China University of Technology, China
  • Soufiana Mekouar, Mohammed V University Rabat, Morocco
  • Baoning Niu, Taiyuan University of Technology, China
  • Frank Pallas, TU Berlin, Germany
  • Lauritz Thamsen, University of Glasgow, United Kingdom
  • Ciprian-Octavian Truică, University Politehnica of Bucharest, Romania
  • Puyu Wang, Northwest University, China
  • Xiangfeng Wang, East China Normal University, China
  • Yangyang Xu, Rensselaer Polytechnic Institute, United States
  • Xiaoming Yuan, Hong Kong University, China
  • Wenbin Zhang, Carnegie Mellon University, United States
  • Chen Zhao, Kitware Company, United States