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Abstract
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Time series data are often clustered repeatedly across various time ranges to mine frequent subsequence patterns from different periods, which could further support downstream applications. Existing state-of-the-art (SOTA) time series clustering method, such as K-Shape, can proficiently cluster time series data referring to their shapes. However, in-database time series clustering problem has been neglected, especially in IoT scenarios with large-volume data and high efficiency demands. Most time series databases employ LSM-Tree based storage to support intensive writings, yet causing underlying data points out-of-order in timestamps. Therefore, to apply existing out-of-database methods, all data points must be fully loaded into memory and chronologically sorted. Additionally, out-of-database methods must cluster from scratch each time, making them inefficient when handling queries across different time ranges. In this work, we propose an in-database adaptation of SOTA time series clustering method K-Shape. Moreover, to solve the problem that K-Shape cannot efficiently handle long time series, we propose Medoid-Shape, as well as its in-database adaptation for further acceleration. Extensive experiments are conducted to demonstrate the higher efficiency of our proposals, with comparable effectiveness. Remarkably, all proposals have already been implemented in an open-source commodity time series database, Apache IoTDB.
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