Big data is an emerging problem in a variety of domains, such remote sensing and cyber. Presently, two directions are explored for handling such big data: advanced algorithms and advanced procedures. Advanced algorithms, e.g. deep learning, aim to find structure in increasingly unstructured data; advanced procedures, e.g. decision fusion, aim to develop logic and approaches to solve problems with existing methods. Of interest to this special issue are improved algorithms, improved procedures, and their combination. The operationally relevant application of methods is of primary interest to illustrate both their practical utility, as well as their theoretical utility.
Applications from business, engineering, medicine, remote sensing and cyber will be solicited.
For our journal link, please visit Sage's webpage: http://journals.sagepub.com/act/site/callforpapers/operationally-relevant-methods-for-big-data-problems
The submitted manuscripts for this special collection will be peer-reviewed before publication.
Find the manuscript submission guidelines here: bit.ly/ACT-guidelines
To submit your manuscript please visit: bit.ly/ACT-Submit
Please submit your paper according to the following timetable for the special collection:
Important dates
Manuscript Deadline
August 31, 2017
First Papers Published
Early 2018
Guest Editors
Dr. William A. Young, youngw1@ohio.edu
Ohio University, USA
Dr. Trevor Bihl, trevor.bihl@wright.edu
Wright State University, USA