title: An insight-based longitudinal study of visual analytics.
abstract: Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysis process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets.
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg 24061-0106, USA. psaraiya@vt.edu
authors.forename: Purvi
authors.initials: P
authors.lastname: Saraiya
authors.forename: Chris
authors.initials: C
authors.lastname: North
authors.forename: Vy
authors.initials: V
authors.lastname: Lam
authors.forename: Karen A
authors.initials: KA
authors.lastname: Duca
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008137:Longitudinal Studies; D012984:Software; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D014796:Visual Perception / Q000502:physiology*