Parallel Exact Time Series Motif Discovery

Abstract

Time series motifs are an integral part of diverse data mining applications including classification, summarization and near-duplicate detection. These are used across wide variety of domains such as image processing, bioinformatics, medicine, extreme weather prediction, the analysis of web log and customer shopping sequences, the study of XML query access patterns, electroencephalograph interpretation and entomological telemetry data mining. Exact Motif discovery in soft real-time over 100K time series is a challenging problem. We present novel parallel algorithms for soft real-time exact motif discovery on multi-core architectures. Experimental results on large scale P6 SMP system, using real life and synthetic time series data, demonstrate the scalability of our algorithms and their ability to discover motifs in soft real-time. To the best of our knowledge, this is the first such work on parallel scalable soft real-time exact motif discovery.