Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes
Summary: Coconut introduces an inverted, sortable z-order summarization that preserves similarity locality, enabling bottom-up, sort-based bulk loading with sequential I/O. Median-based splitting keeps leaves dense, jointly improving construction and query speed while reducing storage versus prior data-series indexes. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Haridimos Kondylakis (Hellas)
- 2. Niv Dayan (Harvard University)
- 3. Kostas Zoumpatianos (Harvard University)
- 4. Themis Palpanas (Paris Descartes University)
BibTeX Citation
@article{kondylakis_vldb18,
title = {{Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes}},
author = {Kondylakis, Haridimos and Dayan, Niv and Zoumpatianos, Kostas and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {6},
pages = {677--690},
doi = {10.14778/3184470.3184472},
url = {https://doi.org/10.14778/3184470.3184472},
year = {2018}
}
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