Informace o publikaci

Data-driven Learned Metric Index: an Unsupervised Approach

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SLANINÁKOVÁ Terézia ANTOL Matej OĽHA Jaroslav DOHNAL Vlastislav

Rok publikování 2021
Druh Článek ve sborníku
Konference 14th International Conference on Similarity Search and Applications (SISAP 2021)
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
Klíčová slova Index structures; Learned index; Unstructured data; Content-based search; Metric space; Machine learning
Přiložené soubory
Popis Metric indexes are traditionally used for organizing unstructured or complex data to speed up similarity queries. The most widely-used indexes cluster data or divide space using hyper-planes. While searching, the mutual distances between objects and the metric properties allow for the pruning of branches with irrelevant data -- this is usually implemented by utilizing selected anchor objects called pivots. Recently, we have introduced an alternative to this approach called Lear\-ned Metric Index. In this method, a series of machine learning models substitute decisions performed on pivots -- the query evaluation is then determined by the predictions of these models. This technique relies upon a traditional metric index as a template for its own structure -- this dependence on a pre-existing index and the related overhead is the main drawback of the approach. In this paper, we propose a data-driven variant of the Learned Metric Index, which organizes the data using their descriptors directly, thus eliminating the need for a template. The proposed learned index shows significant gains in performance over its earlier version, as well as the established indexing structure M-index.
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