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ASH 2014 : Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH) in Conjunction with 2014 IEEE Conference on Big Data

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Link: https://www.tacc.utexas.edu/news/events/2014/ieee/software-hardware-big-data
 
When Oct 24, 2014 - Oct 27, 2014
Where Washington DC, USA
Submission Deadline Aug 30, 2014
 

Call For Papers

Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)
in Conjunction with 2014 IEEE Conference on Big Data
Introduction

Hailed by some as the fourth paradigm in science, data-intensive science has brought a profound transformation to scientific research. Indeed, the data-driven discovery has already happened in various research fields, such as earth sciences, medical sciences, biology and physics, to name just a few. It is expected that 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 will also require substantial computational resources in the process. However, many domain sciences still mostly rely on traditional experimental paradigms. It is often a major challenge to transform a solution obtained 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 domain scientists, especially those in the fields that traditionally lack computation and programming, but have nonetheless become the 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 for big data problems. These new hardware brings new opportunities for performance improvement but also new challenges. While those technologies have the potential to greatly improve the capabilities of big data analytics, such potential are often not fully realized. Due to the cost, sophistications of those technology, and limited initial application support, the new technologies often seem remote to the end users and are not fully utilized in the academia years after their invention. It is therefore very important to make those technologies understood and accessible by data scientists in a timely manner.

Meanwhile, comprehensive analytic software packages and programming environments, have become increasingly popular as open-source platforms for data analysis. Most data scientists have had experiences with small to medium data and now facing the challenges posed by Big Data. Those software not only provide collection of analytic methods but also have the potential to utilize new hardware transparently and reduce the efforts required of the end users. For example, R has traditionally been the programming language preferred by data scientists. Recently members of the R and HPC communities have tried to step up to big data with R, resulting in methods for effectively adapting R to a variety of high-performance and high-throughput computing technologies. Parallel to these developments, a family of software frameworks (e.g., Apache Spark, Airavata) has been developed for executing and managing computational jobs and workflows on distributed computing resources, while providing web-based science gateways to assist domain scientists to compose, manage, execute, and monitor big data applications and workflows composed of these services.

This workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH) aims to connect the latest hardware and software developments with the end users of big data. It focuses on the accessibility and applicability of the latest hardware and software to practical domain problems and hence directly facilitates domain researchers' data driven discovery. The issues in discussion include performance evaluation, optimizations, accessibility and usability of new technologies. The participants will consist of computer scientists, domain users, service providers, as well as technology inventors in industry. The constituents of the workshop will advance direct and productive communication between cyber-infrastructure specialists and data scientists who normally work separately.
Topics of interest include, but are not limited to:

Adopting latest hardware technology with for Big Data analytics
Application and use cases in using cyber-infrastructure for Big Data in sciences and engineering
Performance tuning with new hardware infrastructure and software platform
Advances in hardware technology
Novel software platforms and models for big data collection management and analysis
Search and data retrieval on large scale data set
Service oriented architectures to enable data science
Science gateway for domain big data research
Big Data and interactive analysis languages (e.g., R, Python, and Matlab)

Paper Submission Guidelines

Please submit a full-length paper (unto 9 page IEEE 2-column format) through the online submission system: http://goo.gl/oAzomX
Formatting Instructions

8.5" x 11" (DOC, PDF)
LaTex Formatting Macros
Important Dates

Aug 30, 2014: Due date for full workshop papers submission

Sept 20, 2014: Notification of paper acceptance to authors

Oct 5, 2014: Camera-ready of accepted papers

October 27-30 2014: Workshops
Workshop Organizers

Chair: Weijia Xu, Texas Advanced Computing Center, University of Texas at Austin
Co-Chair: Hui Zhang, IU/Pervasive Technology Institute
Program Committee Members

Eli Collins, Cloudera, USA
Cheqing Jin, East China Normal University, China
Xu Liu, Rice University, USA
Xiaoyi Lu, Ohio State University, USA
Rui Mao, National High Performance Computing Center at ShenzhenShenzhen University, P. R. China
Nirav Merchant, University of Arizona, USA
George Ostrouchov, ORNL/NICS University of Tennessee, USA
Marlon Pierce, Indiana University, USA
Keshav Pingali, University of Texas at Austin, USA
Andrew Purtell, Intel, USA
Smriti Ramakrishnan, Oracle, USA
Ananth Sankaranarayanan, Intel, USA
J. Ray Scott, Pittsburgh Supercomputing Center, Carnegie Mellon University, USA
Li Shen, Indiana University School of Medicine, USA
Raminder Singh, Indiana University, USA
Dan Stanzione, Texas Advanced Computing Center, USA
Steve Wong, Houston Methodist Hospital, USA
Hongfeng Yu, University of Nebraska-Lincoln, USA

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