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BTSD 2020 : DEADLINE EXTENDED! The 2nd International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 2020

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Link: https://sites.google.com/view/btsd2020/home
 
When Dec 10, 2020 - Dec 13, 2020
Where Virtual
Abstract Registration Due Oct 9, 2020
Submission Deadline Oct 15, 2020
Notification Due Nov 1, 2020
Final Version Due Nov 15, 2020
Categories    machine learning   physics   material science   big data
 

Call For Papers

The 2nd International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 2020
in conjunction with 2020 IEEE International Conference on Big Data (IEEE BigData 2020)

December 10-13, 2019 @ Taking Place Virtually

Workshop Date/Time: TBD

Call for Papers

Program Co-chairs

Sangkeun (Matt) Lee, Computational Data Analytics Group, Computer Science and Mathematics Division, Oak Ridge National Laboratory, lees4@ornl.gov
Travis Johnston, Computational Data Analytics Group, Computer Science and Mathematics Division, Oak Ridge National Laboratory, johnstonjt@ornl.gov

Introduction to Workshop

Advances in big data technology, artificial intelligence, and machine learning have created so many success stories in a wide range of areas, especially in industry. These success stories have been motivating scientists, who study physics, chemistry, materials, medicine and many more, to explore a new pathway of utilizing big data tools for their scientific activities.

However, there are barriers to overcome. Most existing big data tools, systems, and methodologies have been developed without considering scientific purposes or scientists’ specific requirements. They are not originally developed for scientists who have no or little knowledge of programming or computer science. On the other hand, for computer scientists, understanding the domain problem is often very challenging due to the lack of enough background knowledge.

We expect that big data technologies can play a great role in contributing to scientific innovation in many ways. There are already a lot of ongoing scientific projects around the world that aim to discover novel hypotheses, analyze big multidimensional data which couldn’t be handled by manually, and reduce the time required by complex calculations via machine. This workshop intends to bring domain scientists and computer scientists together while exploring and extending opportunities in the development of big data tools, systems, and methodologies for scientific discovery, to share success stories and lessons learned, and discuss challenges, which if overcome would enable successful collaboration across different domains, especially domain scientists and computer/data scientists.

In this workshop, we discuss the following questions:
- What makes big data tools for scientists different from the existing tools?
- What specific needs and challenges do domain scientists face when they try to adopt big data tools?
- How can computer scientists and domain scientists communicate to define a feasible problem together?
- What are the barriers of using big data for scientific discovery and how do these barriers differ in different science domains?
Workshop History

- The first international workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) was held in December 2019 in conjunction with IEEE Big Data 2019 conference, organized by Matt Lee and Travis Johnston. We received a total of 26 submissions, 12 papers were accepted. Each presenter was given 15 minutes and there were very active Q&A and discussion sessions. It was a great start to build a strong scientific collaboration community, and we would like to continue this in the second workshop.

Research Topics Included in the Workshop
- Big data tools, systems, and methods related to, but not limited to:
- Scientific data processing
- Artificial intelligence/Deep neural networks/Machine learning
- Text mining/Graph mining
- Database/Query processing/Query Optimization
- Parallel computation/High Performance Computing
- Visualization/User Interface/HCI
- Parallelization/Performance/Scalability
- High Performance Computing …
that facilitate innovation and discovery in a scientific domain, such as:
- Physics
- Chemistry
- Material science
- Mechanical engineering
- Nuclear engineering
- Biomedical science …
- Use cases, success stories, lessens learned in scientific discovery using big data tools, systems, and methods

Program Committee Members
- Tom Potok, Oak Ridge National Laboratory
- Da Yan, University of Alabama Birmingham
- Sisi Duan, University of Maryland, Baltimore County
- Feng Bao, Florida State University
- Minsuk Kahng, Oregon State University
- Youngjae Kim, Sogang University, Seoul, Republic of Korea
- Kangil Kim, GIST(Gwangju Institute of Science & Technology)
- Ramakrishnan Kannan, Oak Ridge National Laboratory
- Sreenivas Rangan Sukumar, Cray Inc.
- Seungha Shin, University of Tennessee
- Alina Lazar, Youngstown State University

Paper Submission
- Please submit a short paper (up to 4 page IEEE 2-column format) or full paper (up to 8 page IEEE 2-column format) through the online submission system.

Paper Submission Page

- Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).

Formatting Instructions

8.5" x 11" (DOC, PDF)

LaTex Formatting Macros

Important Dates
Oct 9 (Updated), 2020 Due date for abstract submission
Oct 15 (Updated), 2020 Due date for short/full workshop papers submission
Nov 1, 2020 Notification of paper acceptance to authors
Nov 15, 2020 Camera-ready of accepted papers

Location
Taking Place Virtually (TBD)

Agenda
TBD

Workshop Primary Contact
Sangkeun (Matt) Lee, Computational Data Analytics Group, Computer Science and Mathematics Division, Oak Ridge National Laboratory, TN, USA. Tel: +1 865 574 8858 Email: lees4@ornl.gov

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