Evaluating Data‐type Heterogeneity in Interactive Visual Analyses with Parallel Axes. (20th January 2022)

• Record Type: Journal Article
• Authors: Matute, José; Linsen, Lars
• Is Part Of: Computer graphics forum. Volume 41:Number 1(2022)
• Page Start: 335
• Page End: 349
• Publication Date: 2022-01-20
• Subjects: information visualization -- visual analytics -- visualization -- user studies -- interaction; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14438
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: The application of parallel axes for the interactive visual analysis of multidimensional data is a widely used concept. While multidimensional data sets are commonly heterogeneous in nature, i.e. data items contain both numerical and categorical (including ordinal) attribute values, the use of parallel axes often assumes either numerical or categorical attributes. While Parallel Coordinates and their large variety of extensions focus on numerical data, Parallel Sets and related methods focus on categorical attributes. While both concepts allow for displaying heterogeneous data, no clear strategies have been defined for representing categories in Parallel Coordinates or discretization of continuous ranges in Parallel Sets. In practice, type conversion as a pre‐processing step can be used as well as coordinated views of numerical and categorical data visualizations. We evaluate traditional and state‐of‐the‐art approaches with respect to the interplay of categorical and numerical dimensions for querying probability‐based events. We also compare against a heterogeneous Parallel Coordinates/Parallel Set approach with a novel interface between categorical and numerical axes . We show that approaches for mapping categorical data to numerical axis representations can lead to lower accuracy in answering probability‐based questions and higher response times than hybrid approaches in multiple‐event scenarios. Abstract : The application of parallel axes for the interactive visual analysis of multidimensional data is a widely used concept.