Low-level components of analytic activity in information visualization

By R. Amar, James R. Eagan, John T. Stasko. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., 2005

864 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/180e548520d2091beb8bb039473bd542e7de5aec

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Citations in the corpus

Fluid interaction for information visualization (2011, neutral): “For example, although Amar et al.(13) present a meta-level review of typical analysis tasks and Yi et al.”
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, background): “These are compound tasks that consist of several smaller subtasks [2].”
VLAT: Development of a Visualization Literacy Assessment Test (2017, methodology): “The topic of visualization literacy is gaining recognition within the Information Visualization community, and a number of researchers have begun to explore this topic [8] [10] [33].”
Design Study Methodology: Reflections from the Trenches and the Stacks (2012, background): “Although similarly crisp low-level visualization tasks exist, such as correlate, cluster or find outliers [2], reducing a real-world problem to these tasks is challenging and time consuming.”
Visual Analytics: Definition, Process, and Challenges (2008, neutral): “regarding the main data types and user tasks [2] to be supported are highly desirable for shaping visual analytics research.”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “user tasks[4] to be supported are highly desirable for shaping visual analytics research.”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “If activity-oriented [40] or low-level [2] task taxonomies were expressed as constraints, they could be used in automated design systems.”
Natural Language to Code Generation in Interactive Data Science Notebooks (2022, methodology): “In addition, other works have also considered applications such as synthesizing visualization plots (Amar et al., 2005; Wang et al., 2019; Narechania et al., 2020; Fu et al., 2020; Wu et al., 2022b).”
A Nested Model for Visualization Design and Validation (2009, background): “It has been used at multiple levels of abstraction and granularity: • high-level domain: cure disease, provide a good user experience during web search; • lower-level domain: investigate microarray data showing gene expression levels and the network of gene interactions [6], analyze web session logs”
Characterizing Users' Visual Analytic Activity for Insight Provenance (2008, methodology): “In contrast, user-oriented taxonomies typically characterize high-level human cognitive tasks or visual perceptual behaviors (e.g., [ 1 , 2, 9, 23]).”
Toward a Deeper Understanding of the Role of Interaction in Information Visualization (2007, background): “…generalize, identify, locate, rank, reveal, switch) and direct visual organizing and encoding tasks (encode) Amar, Eagan, and Stasko (2005) [4] Retrieve value, filter, compute derived value, find extremum, sort, determine range, characterize distribution, find anomalies, cluster, and…”