Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances
Summary: Large-scale study of lower bounds for elastic time-series distances, extracting four LB desiderata and exposing gaps in prior coverage. GLB: unified framework creating cache-friendly adaptive summaries that capture boundaries unsupervised, yielding up to 13.5× pruning speedups and first LBs for two measures. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. John Paparrizos (Ohio State University)
- 2. Kaize Wu (University of Chicago)
- 3. Aaron Elmore (University of Chicago)
- 4. Christos Faloutsos (Carnegie Mellon University)
- 5. Michael J. Franklin (University of Chicago)
BibTeX Citation
@article{paparrizos_vldb23,
title = {{Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances}},
author = {Paparrizos, John and Wu, Kaize and Elmore, Aaron and Faloutsos, Christos and Franklin, Michael J.},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {8},
pages = {2019--2032},
doi = {10.14778/3594512.3594530},
url = {https://doi.org/10.14778/3594512.3594530},
year = {2023}
}
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