Finding Patterns in Visualized Data by Adding Redundant Visual Information

TL;DR

This paper introduces PATRED, a method that adds redundant visual information to line-charts to improve pattern detection during visual data exploration, validated through comparisons with data scientist judgments.

Contribution

It proposes a novel redundancy-based technique for enhancing pattern recognition in line-charts, evaluated across multiple distance metrics and data perturbations.

Findings

Redundancy improves pattern detection for certain distance metrics.

Some metrics benefit more from redundancy depending on data perturbation.

Adding redundancy enhances visual exploration of time-series data.

Abstract

We present "PATRED", a technique that uses the addition of redundant information to facilitate the detection of specific, generally described patterns in line-charts during the visual exploration of the charts. We compared different versions of this technique, that differed in the way redundancy was added, using nine distance metrics (such as Euclidean, Pearson, Mutual Information and Jaccard) with judgments from data scientists which served as the "ground truth". Results were analyzed with correlations (R2), F1 scores and Mutual Information with the average ranking by the data scientists. Some distance metrics consistently benefit from the addition of redundant information, while others are only enhanced for specific types of data perturbations. The results demonstrate the value of adding redundancy to improve the identification of patterns in time-series data during visual exploration.