A Collaboration between Visual and Automated Analyses of Complex Flow Patterns
Suryatapa Roy, Yaoping Hu · 2018
A well-designed collaboration between visual and automated analyses can facilitate complex tasks performed by an analyst (l.e., user). One such task is the study of spatiotemporal (unsteady) flow fields represented by velocity vectors. Our earlier work introduced a cluster-based technique of abstracting velocity patterns for visual analysis of flows. Though these patterns convey important information, the visual analysis overloads the human cognitive abilities of identifying and tracking patterns in space and time. To address this overloading, we have injected new spatial patterns into the visual analysis and developed an automated analysis to detect the temporal changes in the velocity and spatial patterns. For aiding the user's understanding of the complex relations between the spatial and temporal characteristics of the patterns, this paper presents a collaboration between the visual and automated analyses. To assess this collaboration, we have proposed a usefulness metrics to encompass usability and utility of an analysis process. Using two complex flow datasets with multiple actuations and thousands of time instants, we have conducted a preliminary assessment of the collaboration based on this metrics. The outcomes of the assessment indicate that the collaboration aids an analyst in identifying and tracking pattern changes. Therefore, the collaboration shows potential in facilitating the study of complex flow patterns.