Learned metric index - proposition of learned indexing for unstructured data

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ANTOL Matej OĽHA Jaroslav SLANINÁKOVÁ Terézia DOHNAL Vlastislav

Rok publikování 2021
Druh Článek v odborném periodiku
Časopis / Zdroj Information Systems
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://www.sciencedirect.com/science/article/pii/S0306437921000326
Doi http://dx.doi.org/10.1016/j.is.2021.101774
Klíčová slova Index structures;Learned index;Unstructured data;Content-based search;Metric space
Popis 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.
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