  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.
