About |
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 Paper |
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. |
Hotel Deals |
OTHER BIG DATA EVENTS |
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![]() IEEE HPSC 2021 : 7th IEEE International Conference on High Performance and Smart Computing
New York, USA May 15, 2021 |
![]() BigDaCI 2021 : 6th International Conference on Big Data Analytics, Data Mining and Computational Intelligence
Online Jul 21, 2021 |
![]() ITIOT 2021 : 2nd International Conference on Information Technology and Internet of Things
Chengdu, China Apr 23, 2021 |
OTHER DATA VISUALIZATION EVENTS |
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![]() KITA 2019 : Knowledge IT Artifacts (KITA) in professional communities and aggregations (KITA 2019)
Rome, Italy Oct 28, 2019 |
![]() ACM SAC DBDM Track 2020 : Special Track on Databases and Big Data Management (DBDM) - The 35th ACM SYMPOSIUM ON APPLIED COMPUTING (SAC 2020)
Prague, Czech Republic Mar 30, 2020 |
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![]() DataViz&Science@ILRN 2019 : Special Track on Data Visualization and Engaging Science @ ILRN 2019
London, UK Jun 24, 2019 |
OTHER HIGH PERFORMANCE COMPUTING EVENTS |
![]() IEEE HPSC 2021 : 7th IEEE International Conference on High Performance and Smart Computing
New York, USA May 15, 2021 |
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![]() ICCSA 2021 : 21st International Conference on Computational Science and its Applications
Cagliari, Italy Jul 05, 2021 |
![]() HPCS 2020 : The 2020 International Conference on High Performance Computing & Simulation
Barcelona Oct 26, 2020 |
OTHER DATA SCIENCE EVENTS |
![]() CONF-CDS 2021 : The 2nd International Conference on Computing and Data Science
Kennedy Commons Stanford (Online) Jan 28, 2021 |
![]() DaKM 2021 : 6th International Conference on Data Mining & Knowledge Management
Vancouver, Canada May 29, 2021 |
![]() ICICS 2021 : The 12th International Conference on Information and Communication Systems
Valencia - Spain May 24, 2021 |
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![]() MaSPECS 2021 : Modeling and Simulation for Performance Evaluation of Computer-based Systems
online Jun 09, 2021 |