  Data series motif discovery represents one of the most useful primitives for
data series mining, with applications to many domains, such as robotics,
entomology, seismology, medicine, and climatology, and others. The
state-of-the-art motif discovery tools still require the user to provide the
motif length. Yet, in several cases, the choice of motif length is critical for
their detection. Unfortunately, the obvious brute-force solution, which tests
all lengths within a given range, is computationally untenable, and does not
provide any support for ranking motifs at different resolutions (i.e.,
lengths). We demonstrate VALMOD, our scalable motif discovery algorithm that
efficiently finds all motifs in a given range of lengths, and outputs a
length-invariant ranking of motifs. Furthermore, we support the analysis
process by means of a newly proposed meta-data structure that helps the user to
select the most promising pattern length. This demo aims at illustrating in
detail the steps of the proposed approach, showcasing how our algorithm and
corresponding graphical insights enable users to efficiently identify the
correct motifs. (Paper published in ACM Sigmod Conference 2018.)
