Large-Scale Similarity-Based Time Series Mining - Silva, Diego F.; Batista, Gustavo E. A. P. A.; Keogh, Eamonn (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2018: Anais do XXXI Concurso de Teses e Dissertações, 2018)
Measuring the (dis)similarity between time series is the main procedure of several algorithms for mining this kind of data, which is ubiquitous in the day-by-day of human beings. While providing satisfactory results, similarity-based methods usually suffer from a high time complexity. This work summarizes a thesis on developing algorithms that allow the similarity-based mining of temporal data in a large scale. The contributions of the thesis have implications in several data mining tasks, such as classification, clustering and motif discovery, as well as applications in music data science.