Technometrics
Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
DEVise: integrated querying and visual exploration of large datasets
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An interactive visual query environment for exploring data
Proceedings of the 10th annual ACM symposium on User interface software and technology
Principles for Information Visualization Spreadsheets
IEEE Computer Graphics and Applications
Interactive feature specification for focus+context visualization of complex simulation data
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
VIQING: Visual Interactive QueryING
VL '98 Proceedings of the IEEE Symposium on Visual Languages
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
IEEE Transactions on Visualization and Computer Graphics
Interactive Visual Analysis of Families of Function Graphs
IEEE Transactions on Visualization and Computer Graphics
Generalized selection via interactive query relaxation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supporting the analytical reasoning process in information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
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In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data.