ASH 2017 - 4th Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)

4th Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)
In conjunction with 2017 IEEE Conference on Big Data (IEEE BigData 2017)
Dec. 11-14, 2017-AT-Boston, MA, USA
Introduction
This workshop aims at bridging the latest technology development in hardware and software with big data end users. 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, and education of new technologies. We anticipate workshop participation from computer scientists, domain users, service providers, technology inventors in industry, as well as educators in computer science and computing technology. We intend to invite cyber-infrastructure specialists to share their experience 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 theory, computing foundation, and essential tools and resources.
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 will be publically accessible for the purposes of continued and deeper data analysis. Big Data analytic will result in the development of many new theories and discoveries but also will require substantial computational resources in the process. However, the main stream of many domain sciences still mostly relies on traditional experimental paradigms. It is often a major challenge on its own to transform a solution working on smaller scale on a standalone server into a massively parallel one running on tens, hundreds, or even thousands of servers. It is a crucial issue to make the latest technology advancements in software and hardware accessible and usable to the ultimate the domain scientists, especially those in fields traditionally not strong in computation and programming, who are driving forces of scientific discovery.
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 brings 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 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, the costs, sophistications of those technology and limited initial application support often make them remote to the end users and not fully utilized in academia years later. So it is even more important to make those technologies understood and accessible by data scientists early. Meanwhile, comprehensive open source analytic software environment and platform, such as R and Python, are freely available and have become increasing popular open-source platform for data analysis. Most data scientists have had experience with small to medium data; and now Big Data poses its own challenges in terms of its size. Those software not only providing collection of analytic methods but also has the potential to utilize new hardware transparently and ease the efforts required from the end user.
Following the success of the workshop held with IEEE Big data conference in the past three years, we are looking forward to organizing this workshop again with invited talks and peer reviewed paper presentations. We believe this workshop will bring technology innovators, service providers, domain researchers, and computer science and computing educators together to discuss the research issues in the emerging field of data science with particular focus on how to utilize the latest software and hardware technologies to facilitate data driven science. This unique combination of opportunities and challenges will attract much attention from both academia and industry. This workshop will directly contribute to facilitating data driven discoveries in the near future.
Topics of interest include, but are not limited to
· Adopt latest hardware technology with for Big Data analytics
· Using high performance computing resources, cyber-infrastructures and large system for Big Data to knowledge discovery
· New software schema designs and data models for big data collection management and analysis
· Analysis, visualization, and retrieval on 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)
· Demos of new software tools and hardware technologies
· Putting Expert-in-the-Loop for big data analytics
· Education of data theory, computing foundation, and data infrastructure for data science
Important dates
· Oct. 10, 2017: Due date for full workshop papers submission
· Nov. 5, 2017: Notification of paper acceptance to authors
· Nov. 15, 2017: Camera-ready of accepted papers
· Dec. 11-14, 2017: Workshops