Finalized:Sunday, October 1, 2017
Author(s):Sethi, R.J., Gil, Y.
In this paper, we demonstrate the use of scientific workflows in bridging expertise across multiple domains by re-purposing workflow fragments in the areas of text analysis, image analysis, and analysis of activity in video. We highlight how the reuse of workflows allows scientists to link across disciplines and avail themselves of the benefits of inter-disciplinary research beyond their normal area of expertise. In addition, we present in-depth studies of various tasks, including tasks for text analysis, multimedia analysis involving both images and text, video activity analysis, and analysis of artistic style using deep learning. These tasks show how the re-use of workflow fragments can turn a pre-existing, rudimentary approach into an expert-grade analysis. We also examine how workflow fragments save time and effort while amalgamating expertise in multiple areas such as machine learning and computer vision.
Sethi, R.J., Gil, Y. (2017) Scientific workflows in data analysis: Bridging expertise across multiple domains. Future Generation Computer Systems. Volume: 75, Pages: 256-270. DOI: 10.1016/j.future.2017.01.001This material is based upon work supported by the National Science Foundation under Grant No. 1440323. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.