A Table-Binning Approach for Visualizing the Past

TL;DR

This paper introduces a simple, rank-based visualization method for large, multi-dimensional, time-varying datasets that uses binning and pattern matching to reveal temporal patterns in a global context.

Contribution

It presents a novel table-binning visualization approach that simplifies complex temporal data and enables pattern matching, improving data exploration over traditional line plots.

Findings

Effective visualization of large temporal datasets.

Pattern matching reveals meaningful temporal profiles.

Compared favorably to classic line plots and SAX.

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