ELPIS: Graph-Based Similarity Search for Scalable Data Science
Summary: ELPIS combines data-series tree indexing with graph-based high-dimensional vector search for scalable in-memory ng-approximate similarity search. It builds indexes 3–8× faster with 40% less memory, while delivering 0.99 recall and up to 10× faster 1-NN queries. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ilias Azizi (Mohammed V University; Université Paris Cité)
- 2. Karima Echihabi (Mohammed V University)
- 3. Themis Palpanas (Institut universitaire de France; Université Paris Cité)
BibTeX Citation
@article{azizi_vldb23,
title = {{ELPIS: Graph-Based Similarity Search for Scalable Data Science}},
author = {Azizi, Ilias and Echihabi, Karima and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {6},
pages = {1548--1559},
doi = {10.14778/3583140.3583166},
url = {https://doi.org/10.14778/3583140.3583166},
year = {2023}
}
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