IEEE TETC SI 2017 : IEEE Transactions on Emerging Topics in Computing, Special Issue on Scholarly Big Data

IEEE TETC SI 2017 : IEEE Transactions on Emerging Topics in Computing, Special Issue on Scholarly Big Data
IEEE TETC SI 2017 : IEEE Transactions on Emerging Topics in Computing, Special Issue on Scholarly Big Data
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Homepage:	https://www.computer.org/cms/Computer.org/transactions/cfps/cfp tetcsi sbd.pdf
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Important dates
Submissions:	2017/12/01T12:00:00
Table of Contents

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CALL FOR PAPERS IEEE Transactions on Emerging Topics in Computing Special Issue on Scholarly Big Data

IEEE Transaction on Emerging Topics in Computing (TETC) seeks original manuscripts for a Special Issue/Section on Scholarly Big Data scheduled to appear in the fourth issue of 2018.

Recent years have witnessed the rapid growth of scholarly information due to advancements in information and communication technologies. Scholarly big data is the vast quantity of research output, which can be acquired from digital libraries, such as journal articles, conference proceedings, theses, books, patents, experimental data, etc. It also encompasses various scholarly related data, such as author demography, academic social networks, and academic activity. The abundance of scholarly data sources enables researchers to study the academic society from a big data perspective. The dynamic and diverse nature of scholarly big data requires different data management techniques and advanced data analysis methods. Today’s researchers realize that new scholarly-big-data specific platform/management/techniques/ are needed. Therefore, a set of emerging topics such as scholarly big data acquisition, storage, management and processing are important issues for the research community. Manuscripts submitted to TETC should be computing focused.

This special issue focuses on covering the most recent research results in scholarly big data management and computing. The issue welcomes both theoretical and applied research (e.g. platforms and applications). It will encourage the effort to share data, advocate gold-standard evaluation among shared data, and promote the exploration of new directions. Topics of interest include (but not limited to):

• New approaches to search and crawling of scholarly big data from various data

sources

• Methods for storing, indexing, and query processing for scholarly big data
• Practices for scholarly big data management and sharing
• Heterogeneous scholarly big data source integration, especially for novel

datasets (e.g. online social media)

• Scholarly big data analysis, mining, and visualization
• Design of next generation scholarly big data platforms and systems
• Algorithms for measuring the scientific impact of articles, authors,

institutions, etc.

• Scientific information network analysis
• Recommendation tools and techniques
• Scientific community detection and clustering
• Graph and text mining in scholarly big data
• Privacy and security issues
• Services and applications

Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. As an author, you are responsible for understanding and adhering to the IEEE submission guidelines. You can access them at the IEEE Computer Society web site, www.computer.org. These should be carefully read before manuscript submission. Please submit your manuscript to Manuscript Central at https://mc.manuscriptcentral.com/tetc-cs

Please note the following important dates. Submission Deadline: Dec. 1, 2017 Reviews Completed: Mar. 1, 2018 Major Revisions Due (if Needed): April 1, 2018 Reviews of Revisions Completed (if Needed): May 1, 2018 Minor Revisions Due (if Needed): June 1, 2018 Notification of Final Acceptance: August 1, 2018 Publication Materials for Final Manuscripts Due: Sept 1, 2018 Publication date: Last Issue of 2018 (December Issue)

Guest Editors

Feng Xia Dalian University of Technology, China f.xia@ieee.org

Huan Liu Arizona State University, USA huanliu@asu.edu

C. Lee Giles Pennsylvania State University, USA giles@ist.psu.edu

Kuansan Wang Microsoft Research, USA Kuansan.Wang@microsoft.com