DataMeadow: a visual canvas for analysis of large-scale multivariate data
Journal article, 2008

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

Visual analytics

Dynamic queries

Multivariate data

Progressive analysis

Starplots

Parallel coordinates

Author

Niklas Elmqvist

University of Paris-Sud

Laboratoire de Recherche en Informatique

John Stasko

Georgia Institute of Technology

Philippas Tsigas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Information Visualization

1473-8716 (ISSN) 1473-8724 (eISSN)

Vol. 7 1 18-33

Subject Categories

Software Engineering

Information Science

Human Computer Interaction

Computer Science

DOI

10.1057/palgrave.ivs.9500170

More information

Latest update

8/1/2018 1