PEASH workshop has been co-located with IEEE BigData Conference since 2014.
7th Workshop on Performance Engineering with Advances in Software and Hardware for Big Data Sciences (PEASH'20)
In conjunction with 2020 IEEE Conference on Big Data (IEEE BigData 2020)
December 10-13, 2020, Atlanta, GA, USA
Following the success of the PEASH (formerly ASH) workshop series co-located with IEEE Big data conference in the past six years, we are looking forward to organizing the 7th PEASH workshop in 2020.
The PEASH workshop has positioned itself as a unique forum for bringing the latest technology development in hardware and software to enable big data science. The topics of the workshop are centered on the accessibility and applicability of the latest hardware and software to practical domain problems and education settings. The workshop will discuss issues in facilitating data-driven discovery with the latest software and hardware technologies for domain researchers, such as performance evaluation, optimization, accessibility, usability, application, and education of new technologies. The presentations and discussions at the workshop will speed and promote the adoption of latest software and hardware technologies for domain researchers working on big data science.
Data-intensive science has become the fourth paradigm in science and has brought a profound transformation of scientific research. Indeed, data-driven discovery has already happened in various research fields, such as earth sciences, medical sciences, biology, and physics, to name a few. In brief, a vast volume of scientific data captured by new instruments has been becoming publically accessible for the purposes of continued and deeper data analysis. Big Data analytics result in the development of many new theories and discoveries but also require substantial computational resources in the process. However, the mainstream of many domain sciences still mostly relies on traditional experimental paradigms. It is a crucial issue to make the latest technology advancements in software and hardware accessible and usable to the domain scientists.
Fueled by the big data analytics needs, new computing and storage technologies are also in rapid development and pushing for new high-end hardware geared for solving big data problems. These new hardware advances bring new opportunities for performance improvement but also new challenges. The overall performance bottleneck of a problem can be shifted, requiring different workload balancing strategy due to the significant performance boost of a particular hardware. While those technologies have the potential to greatly improve the capabilities in big data analytics and make significant contributions to data-driven science, it is even more important to make those technologies understood and accessible by data scientists early.
In the recent years, analysis algorithms and software for machine learning have boomed. Deep neural network based methods begin to make buzz in nearly every domain fields. There are a dozen open source deep learning frameworks developed in last year alone. Comprehensive open source analytic software environments and platforms are also evolving with these new developments for data science. Therefore, how to efficiently utilize these latest technologies to solve big data problems in scientific domains and how to facilitate continuing innovations in computer science with these latest technologies are two central focuses of this workshop.
We anticipate workshop participation from computer scientists, domain users, service providers, educators, and technology practitioners in industry. We intend to invite cyber-infrastructure specialists to share their experiences with the latest hardware and software advancements, data scientists to share their experiences and perspectives in using those technologies for data-driven discovery, and educators to share their stories in educating big data theories, computing foundations, and essential tools and resources.
Call for contribution:
We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on performance engineering, including the performance engineering for challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The workshop adopts single-blind review policy. Accepted and presented paper at PEASH'20 will be be published in the proceedings of IEEE BigData'20. We expect to have a very high quality and exciting technical program at Atlanta this year.
Research Topics of interest include, but are not limited to
Adopt latest hardware technology (e.g., multicore, gpgpu, Intel coprocessors, HPC, cloud) for Big Data analytics
Using high performance computing resources, cyber-infrastructures, and large systems for accelerating data to knowledge discovery
Performance analysis, engineering, and evaluation for big data solutions
Analysis, visualization, and retrieval of large-scale data sets
Application and use cases in using novel tools and resources for Big Data in sciences and engineering
Service-oriented architectures to enable data science
Big data and interactive analysis languages (e.g., R, Python, Scala, and Matlab) and cloud-based analytical platforms
Demonstrations and evaluations of latest software tools and hardware technologies
Education of data theory, computing foundation, and data infrastructure for data science
Applications and methods of machine learning and deep neuron networks with big data set