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 @ 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. 30, 2017: Due date for full workshop papers submission
·
Nov. 15, 2017: Notification of paper acceptance to authors
·
Nov. 20, 2017: Camera-ready of accepted papers
·
Dec. 11-14, 2017: Workshops
Program
chairs
· Weijia Xu (Texas Advanced Computing Center)
· Hui Zhang (University of Louisville)
·
Hongfeng Yu (University of Nebraska)
Program
Committee
· Dan Stanzione (Texas Advanced Computing Center)
· Eric Wernert (Pervasive Technology Institute/Indiana University)
·
Nirav Merchant (University of Arizona)
· J. Ray Scott (Pittsburg Supercomputing Center)
·
Ian Foster (Argonne
National Laboratory)
·
George Ostrouchov (Oak Ridge National Lab/UTK)
·
Jian Li (Huawei
Technology Inc.)
·
Avishkar Misra (Oracle Inc.)
· Dhabaleswar K. Panda (Ohio State University)
· Chaoli Wang (University of Notre Dame)
· Robert Hsu (Chung Hua University, Taiwan)
· Frank Zou (Worcester Polytechnic Institute)
· Cherry Liu (Georgia Tech)
· Guangchen Ruan (Research Technology, Indiana University)
· Tiejun Li (National University of Defense Technology, China)
· Rui Mao (Shenzhen University, China)
Paper
Submission
|
1) |
Papers
should be formatted to 10 pages IEEE
Computer Society Proceedings Manuscript Formatting Guidelines (see link to
"formatting instructions" below). |
|
2) |
Although we accept
submissions in the form of PDF, PS, and DOC/RTF files, you are strongly encouraged |
Final
Program (TBA)
Registration
To attend the workshop, you will need to
register with
the IEEE Big Data 2017 Conference.
Hotel
Information
IEEE Big Data 2017 will take place at the Westin Copley Place, Boston from December 11-14, 2017. Please check IEEE Big Data 2017 Conference Hotel page for conference rate.
The Westin Copley Place, Boston
10 Huntington Avenue, Boston, MA, 02116, United States.
Previous
ASH Workshops
·
ASH 2016
·
ASH
2015
·
ASH
2014