VALMOD: Scalable Discovery of Variable-Length Motifs in Data Series

Michele Linardi*,Yan Zhu+

Themis Palpanas* and Eamonn Keogh+

*(Lipade, University Paris Descartes) +(UC, Riverside)

In a nutshell...

VALMOD is a scalable motif discovery algorithm that efficiently finds all motifs 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. Removing the unrealistic assumption that the user knows the correct length, can often produce more intuitive and actionable results, which could have been missed otherwise.

This page is the support page of VALMOD, which mainly complements the experimental evaluation, providing the relative materials. You may find the VALMOD article here: VALMOD 2018 (ACM Sigmod conference) .

Empirical evaluation of our approach

In this following part, we provide the source code of VALMOD (in C), and all the DATASETS used for evaluating our system.

Please note that we compared VALMOD with other motif discovery approaches:
Since the first two methods were proposed by different authors, we invite the interested user to ask them the source code.

You can find several experimental "ready-to-run" bash scripts in this ARCHIVE . Both the zip files (source code and experiments scripts) are protected by password. Please contact me at michele[dot]linardi[at]orange[dot]fr.