  In the last fifteen years, data series motif and discord discovery have
emerged as two useful and well-used primitives for data series mining, with
applications to many domains, including robotics, entomology, seismology,
medicine, and climatology. Nevertheless, the state-of-the-art motif and discord
discovery tools still require the user to provide the relative length. Yet, in
several cases, the choice of length is critical and unforgiving. Unfortunately,
the obvious brute-force solution, which tests all lengths within a given range,
is computationally untenable. In this work, we introduce a new framework, which
provides an exact and scalable motif and discord discovery algorithm that
efficiently finds all motifs and discords in a given range of lengths. We
evaluate our approach with five diverse real datasets, and demonstrate that it
is up to 20 times faster than the state-of-the-art. Our results also show that
removing the unrealistic assumption that the user knows the correct length, can
often produce more intuitive and actionable results, which could have otherwise
been missed. (Paper published in Data Mining and Knowledge Discovery Journal -
2020)
