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Data series motif discovery represents one of the most useful primitives fordata series mining, with applications to many domains, such as robotics,entomology, seismology, medicine, and climatology, and others. Thestate-of-the-art motif discovery tools still require the user to provide themotif length. Yet, in several cases, the choice of motif length is critical fortheir detection. Unfortunately, the obvious brute-force solution, which testsall lengths within a given range, is computationally untenable, and does notprovide any support for ranking motifs at different resolutions (i.e.,lengths). We demonstrate VALMOD, our scalable motif discovery algorithm thatefficiently finds all motifs in a given range of lengths, and outputs alength-invariant ranking of motifs. Furthermore, we support the analysisprocess by means of a newly proposed meta-data structure that helps the user toselect the most promising pattern length. This demo aims at illustrating indetail the steps of the proposed approach, showcasing how our algorithm andcorresponding graphical insights enable users to efficiently identify thecorrect motifs. (Paper published in ACM Sigmod Conference 2018.)

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series