In-Database Time Series Clustering

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.