A Novel Parallel Scheme for Fast Similarity Search in Large Time Series

The similarity search is one of the fundamental components in time series data mining, e.g. clustering, classification, association rules mining. Many methods have been proposed to measure the similarity between time series, including Euclidean distance, Manhattan distance, and dynamic time warping (DTW). In contrast, DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series. However, due to its quadratic time and space complexity, DTW is not suitable for large time series datasets. Many improving algorithms have been proposed for DTW search in large databases, such as approximate search or exact indexed search. Unlike the previous modified algorithm, this paper presents a novel parallel scheme for fast similarity search based on DTW, which is called MRDTW (MapRedcue-based DTW). The experimental results show that our approach not only retained the original accuracy as DTW, but also greatly improved the efficiency of similarity measure in large time series.