A Table-Binning Approach for Visualizing the Past

Nicolas Turenne

Abstract: Large amounts of data are available due to low-cost and high-capacity data storage equipments. We propose a data exploration/visualization method for tabular multi-dimensional, time-varying datasets to present selected items in their global context. The approach is simple and uses a rank-based visualization and a pattern matching functionality based on temporal profiles. Ranking categories can be specified in a flexible way and are used instead of actual values (value reduction into bins) and plotting it over time in an unevenly quantized representation. Patterns that emerge are matched against a set of eight predefined temporal profiles. The graphical summarization of large-scale temporal data is proposed and applicability is tested qualitatively on about eight data sets and the approach is compared to classic line plots and SAX representation