sDTW: Computing DTW Distances using Locally Relevant Constraints based on Salient Feature Alignments

Summary: sDTW detects robust salient features and aligns them to derive locally relevant DTW warp-path constraints, pruning redundant grid computation. Its fixed/adaptive core-width strategies improve distance accuracy and retrieval over feature-agnostic fixed-band heuristics. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 10575
Venue: VLDB
Year: 2012
Pagerank: 5.093636e-05
Overall Rank: 12,329 | 15.42%
DOI: 10.14778/2350229.2350266

Incoming Non-self Citations Over Time

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Authors

1. K. Selçuk Candan (Arizona State University)
2. Rosaria Rossini (University of Torino)
3. Maria Luisa Sapino (University of Torino)
4. Xiaolan Wang (Arizona State University)

BibTeX Citation

@article{candan_vldb12,
        title = {{sDTW: Computing DTW Distances using Locally Relevant Constraints based on Salient Feature Alignments}},
        author = {Candan, K. Selçuk and Rossini, Rosaria and Sapino, Maria Luisa and Wang, Xiaolan},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {11},
        pages = {1519--1530},
        doi = {10.14778/2350229.2350266},
        url = {https://doi.org/10.14778/2350229.2350266},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
371	Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases	1995	VLDB	0.00019869565
601	Exact Indexing of Dynamic Time Warping	2002	VLDB	0.00015870156
898	Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures	2008	VLDB	0.00013339042

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