Their main domain of application is the field of bioinformatics where they are frequently used by researchers in order to elucidate genomic sequence similarities and alignment. However, this useful technique has remained comparatively constrained to domains where the data has an inherent discrete structure (i.e., text).

In this paper we demonstrate how dot plots can be used for the analysis and mining of real-valued time series. We design a tool that creates highly descriptive dot plots which allow one to easily detect similarities, anomalies, reverse similarities, and periodicities as well as changes in the frequencies of repetitions. As the underlying algorithm scales well with the input size, we also show the feasibility of the plots for on-line data monitoring.