Constructing Compact Time Series Index for Efficient Window Query Processing

Analyzing and mining of time series have been widely studied in both academia and industry in recent years. Given a set of long time series, data analysts can utilize the window-based similarity search to explore subsequences in arbitrary time windows. Existing techniques are not efficient for window-based query processing. In particular, the whole matching index approach needs to build an individual index for each window, which incurs huge space cost. The existing window-based approach can only cluster neighboring windows, which leads to loose bounds of each group, and thus degrades the query processing efficiency. In this paper, we propose a compact time series index (WinIdx) for efficient window query processing. Specifically, i) we propose a novel distance measurement to capture the similarity between windows, ii) WinIdx provides a compact index structure for windows within a cluster by exploiting the similarity among subsequences relationships, and iii) several optimizations (e.g., sortable summarization, summarization envelop) are equipped in WinIdx to improve the efficiency of index construction, query processing and index footprints. We conduct extensive experiments on both real and synthetic time series to demonstrate the superiority of WinIdx against state-of-the-art approaches.