Designing and Evaluating Scientific Workflows for Big Data Interactions

This paper explores the specialized nature of research-oriented web applications that enable interactions with and the visual analysis of ``Big Data,´´ i.e., large, heterogeneous scientific datasets. We introduce a pragmatic methodology for the design and evaluation of scientific workflows in research-oriented web applications. Through an in-depth usability study of the CoGe web application, a system that provides a rich set of tools for exploring genomic datasets, we demonstrate: how to identify bottlenecks in multi-step tasks; how to analyze these bottlenecks in order to provide effective solutions for improving user experience; and how these solutions may more generally apply to similar research-oriented websites in other scientific domains that also enable scientific workflows. Specifically, we provide details regarding: our user interviews, the visualization system we created to analyze complex tasks associated with scientific workflows, and how this analysis directly leads to suggestions for improvements in the current implementation of the CoGe web application. A follow-up study was carried out which indicates that our suggestions improved the ability of CoGe users to navigate and complete custom workflows, leading us to believe that our approach could also be applied to other research-oriented web applications that utilize scientific datasets.