A Table-Binning Approach for Visualizing the Past
This is an incremental method for data analysts needing to explore and visualize multi-dimensional temporal datasets in a simplified, pattern-based manner.
The authors tackled the problem of visualizing large-scale, time-varying tabular data by proposing a table-binning method that reduces values into bins and plots them over time with pattern matching against predefined temporal profiles. The result is a graphical summarization approach tested qualitatively on about eight datasets, showing applicability compared to classic line plots and SAX representation.
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