In the big data era, large amounts of time series data have been collected from scientific experiments and business operations. With the fast growth of data volume, significant challenges in pattern recognition has emerged in diverse time series analysis systems. Since existing methods are inefficient and not applicable to large-scale and especially incremental time series, this paper proposes a novel and efficient pattern recognition method named MDLits for large-scale and incremental time series. It eliminates most redundant computation in the related works, and re-uses existing information by exploiting the correlation between existing data and newly-arrived ones. A Hadoop platform is implemented for clinical electrocardiography classification. The experiments on practical healthcare data show that our method outperforms the related arts in processing time, precision and recall. Moreover, it can scale to a large size and fit to incremental time series, which demonstrates the effectiveness and scalability of our method and system.
['Bo Liu', 'Jianqiang Li', 'Ji-Jiang Yang', 'Jing Bi', 'Rong Li', 'Yong Li']
Pattern Recognition for Large-Scale and Incremental Time Series in Healthcare