Data-driven Learned Metric Index: an Unsupervised Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00119191" target="_blank" >RIV/00216224:14330/21:00119191 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_7" target="_blank" >http://dx.doi.org/10.1007/978-3-030-89657-7_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_7" target="_blank" >10.1007/978-3-030-89657-7_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven Learned Metric Index: an Unsupervised Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data-driven Learned Metric Index: an Unsupervised Approach

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GA19-02033S" target="_blank" >GA19-02033S: Vyhledávání, analytika a anotace datových toků lidských pohybů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

14th International Conference on Similarity Search and Applications (SISAP 2021)

ISBN

9783030896560

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

14

Strana od-do

81-94

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Dortmund, Germany

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000722252200007