Analyzing　time　series　data　can　reveal　the　temporal　behavior　of　the　underlying　mechanism　producing　the　data.　Time　series　motifs,　which　are　similar　subsequences　or　frequently　occurring　patterns,　have　significant　meanings　for　researchers　especially　in　medical　domain.　With　the　fast　growth　of　time　series　data,　traditional　methods　for　motif　discovery　are　inefficient　and　not　applicable　to　large-scale　data.　This　work　proposes　an　efficient　Motif　Discovery　method　for　Large-scale　time　series　(MDLats).　By　computing　standard　motifs,　MDLats　eliminates　a　majority　of　redundant　computation　in　the　related　arts　and　reuses　existing　information　to　the　maximum.　All　the　motif　types　and　subsequences　are　generated　for　subsequent　analysis　and　classification.　Our　system　is　implemented　on　a　Hadoop　platform　and　deployed　in　a　hospital　for　clinical　electrocardiography　classification.　The　experiments　on　real-　world　healthcare　data　show　that　MDLats　outperform　the　state-of-the-art　methods　even　in　large　time　series.