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        <identifier>oai:drops-oai.dagstuhl.de:17028</identifier>
        <datestamp>2024-03-06T10:59:00Z</datestamp>
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          <dc:title>Approximating Dynamic Time Warping Distance Between Run-Length Encoded Strings</dc:title>
          <dc:creator>Xi, Zoe</dc:creator>
          <dc:creator>Kuszmaul, William</dc:creator>
          <dc:subject>Dynamic time warping distance</dc:subject>
          <dc:subject>approximation algorithms</dc:subject>
          <dc:subject>run-length encodings</dc:subject>
          <dc:subject>computational geometry</dc:subject>
          <dc:description>Dynamic Time Warping (DTW) is a widely used similarity measure for comparing strings that encode time series data, with applications to areas including bioinformatics, signature verification, and speech recognition. The standard dynamic-programming algorithm for DTW takes O(n²) time, and there are conditional lower bounds showing that no algorithm can do substantially better. &#13;
In many applications, however, the strings x and y may contain long runs of repeated letters, meaning that they can be compressed using run-length encoding. A natural question is whether the DTW-distance between these compressed strings can be computed efficiently in terms of the lengths k and 𝓁 of the compressed strings. Recent work has shown how to achieve O(k𝓁² + 𝓁 k²) time, leaving open the question of whether a near-quadratic Õ(k𝓁)-time algorithm might exist.&#13;
We show that, if a small approximation loss is permitted, then a near-quadratic time algorithm is indeed possible: our algorithm computes a (1 + ε)-approximation for DTW(x, y) in Õ(k𝓁 / ε³) time, where k and 𝓁 are the number of runs in x and y. Our algorithm allows for DTW to be computed over any metric space (Σ, δ) in which distances are O(log n)-bit integers. Surprisingly, the algorithm also works even if δ does not induce a metric space on Σ (e.g., δ need not satisfy the triangle inequality).</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Zoe Xi and William Kuszmaul</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ESA.2022.90</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-170281</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.90</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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