Visualizing Dimensionally-Reduced Data: Interviews with Analysts and a Characterization of Task Sequences

Authors. Matthew Brehmer, Michael Sedlmair, Stephen Ingram, Tamara Munzner

Venue. CHI BELIV (2014) Workshop Paper

Type. Workshop Paper

Materials. DOI PDF

Abstract. We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.

@inproceedings{10.1145/2669557.2669559,
    title        = {Visualizing Dimensionally-Reduced Data: Interviews with Analysts and a Characterization of Task Sequences},
    author       = {Brehmer, Matthew and Sedlmair, Michael and Ingram, Stephen and Munzner, Tamara},
    year         = {2014},
    booktitle    = {Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization},
    publisher    = {Association for Computing Machinery},
    series       = {BELIV '14},
    pages        = {1–8},
    doi          = {10.1145/2669557.2669559},
    url          = {https://doi.org/10.1145/2669557.2669559},
    abstract     = {We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.},
    numpages     = {8},
    keywords     = {tasks, dimensionally-reduced data, interview study}
}