International Workshop on Big Data Reduction

held with 2020 IEEE International Conference on Big Data

Branching

Introduction

Today’s modern applications are producing too large volumes of data to be stored, processed, or transferred efficiently. Data reduction is becoming an indispensable technique in many domains because it can offer a great capability to reduce the data size by one or even two orders of magnitude, significantly saving the memory/storage space, mitigating the I/O burden, reducing communication time, and improving the energy/power efficiency in various parallel and distributed environments, such as high-performance computing (HPC), cloud computing, edge computing, and Internet-of-Things (IoT). An HPC system, for instance, is expected to have a computational capability of floating-point operations per second, and large-scale HPC scientific applications may generate vast volumes of data (several orders of magnitude larger than the available storage space) for post-anlaysis. Moreover, runtime memory footprint and communication could be non-negligible bottlenecks of current HPC systems.

Tackling the big data reduction research requires expertise from computer science, mathematics, and application domains to study the problem holistically, and develop solutions and harden software tools that can be used by production applications. Specifically, the big-data computing community needs to understand a clear yet complex relationship between application design, data analysis and reduction methods, programming models, system software, hardware, and other elements of a next-generation large-scale computing infrastructure, especially given constraints on applicability, fidelity, performance portability, and energy efficiency. New data reduction techniques also need to be explored and developed continuously to suit emerging applications and diverse use cases.

There are at least three significant research topics that the community is striving to answer: (1) whether several orders of magnitude of data reduction is possible for extreme-scale sciences; (2) understanding the trade-off between the performance and accuracy of data reduction; and (3) solutions to effectively reduce data size while preserving the information inside the big datasets.

The goal of this workshop is to provide a focused venue for researchers in all aspects of data reduction in all related communities to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community.

Please note this year’s IEEE BigData conference and IWBDR workshop will be held virtually. Proceedings of the workshop will be published as planned. We will provide more details about how to attend this workshop virtually soon.

Submissions

Topics of Interest

The focus areas for this workshop include, but are not limited to:

Proceedings

All papers accepted for this workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the IEEE BigData Conference.

Submission Instructions

Important Dates

Organizers

Program Chairs

Web Chair

Jiannan Tian, Washington State University

Planning Program Committee

Program Schedule

TBD