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

TL;DR

VALMOD is a scalable algorithm for discovering variable length motifs in data series, enabling efficient detection and ranking across length ranges, with supportive metadata for motif length selection.

Contribution

It introduces a novel scalable motif discovery method that finds all motifs within a length range and provides a length-invariant ranking, addressing limitations of existing tools.

Findings

Efficiently finds all motifs within specified length ranges.

Provides a length-invariant ranking of discovered motifs.

Includes a meta-data structure to aid motif length selection.

Abstract

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.)