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原文摘要

In applications such as household energy monitoring and ECG analysis, analysts may search for time series that satisfy constraints on trends, value ranges, and durations. To support such queries at scale, we propose TSseek, a distributed processing engine for time series pattern queries. Its regex-inspired language enables users to compose trends, value ranges, and wildcards with constraints on changes between readings and the duration, position, order, and spacing of patterns. During indexing, TSseek approximates each stored time series with error-bounded line segments, records their slope and value-range metadata, and builds TSseek-X, a distributed spatial index over the segments. At query time, TSseek rewrites the query and uses selectivity estimates to plan the index lookup. During execution, index lookup uses spatial probes derived from query constraints over reading positions and values to select candidate series from TSseek-X while guaranteeing to retain every true match. Finally, exact refinement checks each candidate against the full query using its original readings. Experiments on four datasets of up to 200 million time series (25.6 billion readings) show 20x speedup over a distributed exact full scan using the same matcher. In the whole-matching comparison, the adapted baselines each reach at most 57% recall, while TSseek returns the exact answer set.