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Segment-based approach for subsequence searches in sequence databases
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Shape-based retrieval of similar subsequences in time-series databases
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General match: a subsequence matching method in time-series databases based on generalized windows
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Aggregation and comparison of trajectories
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Efficient Similarity Search for Time Series Data Based on the Minimum Distance
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Minimum distance queries for time series data
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A novel technique for indexing video surveillance data
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Bounded similarity querying for time-series data
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Exact indexing of dynamic time warping
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A Multiresolution Symbolic Representation of Time Series
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Optimization of subsequence matching under time warping in time-series databases
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A segment-wise time warping method for time scaling searching
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Quantizing time series for efficient similarity search under time warping
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Discovering and Matching Elastic Rules from Sequence Databases
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Prefix-querying with anL1 distance metric for time-series subsequence matching under time warping
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Exact indexing of dynamic time warping
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Using multiple indexes for efficient subsequence matching in time-series databases
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Similarity Search Algorithm for Efficient Sub-trajectory Matching in Moving Databases
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Bounded similarity querying for time-series data
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A segment-wise time warping method for time scaling searching
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Histogram distance for similarity search in large time series database
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Boundary-based lower-bound functions for dynamic time warping and their indexing
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Similarity search over incomplete symbolic sequences
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We propose an indexing technique for fast retrieval of similar subsequences using time warping distances. A time warping distance is a more suitable similarity measure than the Euclidean distance in many applications, where sequences may be of different lengths or different sampling rates. Our indexing technique uses a disk-based suffix tree as an index structure and employs lower-bound distance functions to filter out dissimilar subsequences without false dismissals. To make the index structure compact and thus accelerate the query processing, we convert sequences of continuous values to sequences of discrete values via a categorization method and store only a subset of suffixes whose first values are different from their preceding values. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than sequential scanning.