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When Oct 29, 2015 - Nov 1, 2015
Where Santa Clara
Submission Deadline Aug 31, 2015
Notification Due Sep 20, 2015
Final Version Due Oct 5, 2015

Call For Papers


In Conjunction with 2015 IEEE Conference on Big Data Conference, Santa Clara, CA

The Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH) workshop 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.

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 in hardware and software 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 and need to be scaled and adapted for Big Data analysis. 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 examples, 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 is the second time that the workshop will be held with IEEE Big Data Conference. The last year conference provided workshop participants with the option for one day registration to attend the workshop only or full conference registration to participate the full conference. All papers accepted by the workshop will be included in the IEEE Big data conference ( proceedings as well and archived in IEEE Xplore digital library. We are also looking for opportunities to invited extended version of workshop to be published in other journals and book chapters. Selected papers from the last year's workshop are invited to submit extended version to be published with Springer.

Topics of Interest

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)


Please submit a full-length paper (up to 8 pages, IEEE 2-column format) through the online submission system.

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

Important Dates

Aug.31, 2015: Due date for full workshop papers submission

Sept 20, 2015: Notification of paper acceptance to authors

Oct 5, 2015: Camera-ready of accepted papers

October 29-Nov -1 2015: Workshop date

Related Resources

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ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
BigData Congress 2018   The 7th International Congress on Big Data
ASH 2017   4th Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)
ASH 2017   ASH 2017 : 4th Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH)
icABCD 2018   International Conference on Advances in Big Data, Computing and Data Communication Systems
Cluster-BigData 2018   Call for book Chapters: Clustering methods for Big Data Analytics: techniques, toolboxes and applications, Springer (USA)
IEEE - ICBDA 2018   2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA 2018)--IEEE Xplore and Ei Compendex
DSA 2018   The Frontiers in Intelligent Data and Signal Analysis DSA 2018
A-MOST 2018   Advances in Model-Based Software Testing