| ANTOL, Matej, Jaroslav OĽHA, Terézia SLANINÁKOVÁ and Vlastislav DOHNAL. Learned metric index - proposition of learned indexing for unstructured data. Information Systems. Elsevier, 2021, vol. 100, No 101774, p. 1-12. ISSN 0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101774. |
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@article{1757938,
author = {Antol, Matej and Oľha, Jaroslav and Slanináková, Terézia and Dohnal, Vlastislav},
article_number = {101774},
doi = {http://dx.doi.org/10.1016/j.is.2021.101774},
keywords = {Index structures;Learned index;Unstructured data;Content-based search;Metric space},
language = {eng},
issn = {0306-4379},
journal = {Information Systems},
title = {Learned metric index - proposition of learned indexing for unstructured data},
url = {http://dx.doi.org/10.1016/j.is.2021.101774},
volume = {100},
year = {2021}
}
TY - JOUR
ID - 1757938
AU - Antol, Matej - Oľha, Jaroslav - Slanináková, Terézia - Dohnal, Vlastislav
PY - 2021
TI - Learned metric index - proposition of learned indexing for unstructured data
JF - Information Systems
VL - 100
IS - 101774
SP - 1-12
EP - 1-12
PB - Elsevier
SN - 03064379
KW - Index structures;Learned index;Unstructured data;Content-based search;Metric space
UR - http://dx.doi.org/10.1016/j.is.2021.101774
N2 - The main paradigm of similarity searching in metric spaces has remained mostly unchanged for decades - data objects are organized into a hierarchical structure according to their mutual distances, using representative pivots to reduce the number of distance computations needed to efficiently search the data. We propose an alternative to this paradigm, using machine learning models to replace pivots, thus posing similarity search as a classification problem, which stands in for numerous expensive distance computations. Even a relatively naive implementation of this idea is more than competitive with state-of-the-art methods in terms of speed and recall, proving the concept as viable and showing great potential for its future development.
ER -
ANTOL, Matej, Jaroslav OĽHA, Terézia SLANINÁKOVÁ and Vlastislav DOHNAL. Learned metric index - proposition of learned indexing for unstructured data. \textit{Information Systems}. Elsevier, 2021, vol.~100, No~101774, p.~1-12. ISSN~0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101774.
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