New Trends in High-D Vector Similarity Search: AI-driven, Progressive, and Distributed
Summary: Tutorial on high-d vector similarity search in the big data era: series, text, multimedia, graphs, embeddings. Surveys AI-driven, progressive, distributed approaches, assesses strengths and weaknesses, and outlines open problems for data-management researchers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Karima Echihabi (Mohammed V University)
- 2. Kostas Zoumpatianos (Harvard University)
- 3. Themis Palpanas (Institut Universitaire de France; University of Paris)
BibTeX Citation
@article{echihabi_vldb21,
title = {{New Trends in High-D Vector Similarity Search: AI-driven, Progressive, and Distributed}},
author = {Echihabi, Karima and Zoumpatianos, Kostas and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {3198--3201},
doi = {10.14778/3476311.3476407},
url = {https://doi.org/10.14778/3476311.3476407},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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