  Comparing many long time series is challenging to do by hand. Clustering time
series enables data analysts to discover relevance between and anomalies among
multiple time series. However, even after reasonable clustering, analysts have
to scrutinize correlations between clusters or similarities within a cluster.
We developed SAX Navigator, an interactive visualization tool, that allows
users to hierarchically explore global patterns as well as individual
observations across large collections of time series data. Our visualization
provides a unique way to navigate time series that involves a "vocabulary of
patterns" developed by using a dimensionality reduction technique,Symbolic
Aggregate approXimation(SAX). With SAX, the time series data clusters
efficiently and is quicker to query at scale. We demonstrate the ability of SAX
Navigator to analyze patterns in large time series data based on three case
studies for an astronomy data set. We verify the usability of our system
through a think-aloud study with an astronomy domain scientist.
