2020/08/31 by Michele Linardi, Yan Zhu, Linardi, Michele +5
Chemistry · Computer Science · #Anomaly Detection Techniques and Applications #Databases (cs.DB) #FOS: Computer and information sciences #Spectroscopy and Chemometric Analyses #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2008.13432
openalex publication_date 2020/08/31 · openalex created_date 2020/09/08 · openalex updated_date 2026/07/28
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.)