Pivot-based approximate k-NN similarity joins for big high-dimensional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10401674" target="_blank" >RIV/00216208:11320/20:10401674 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=n90S2QtvBi" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=n90S2QtvBi</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.is.2019.06.006" target="_blank" >10.1016/j.is.2019.06.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pivot-based approximate k-NN similarity joins for big high-dimensional data

Popis výsledku v původním jazyce

Given an appropriate similarity model, the k-nearest neighbor similarity join represents a useful yet costly operator for data mining, data analysis and data exploration applications. The time to evaluate the operator depends on the size of datasets, data distribution and the dimensionality of data representations. For vast volumes of high-dimensional data, only distributed and approximate approaches make the joins practically feasible. In this paper, we investigate and evaluate the performance of multiple MapReduce-based approximate k-NN similarity join approaches on two leading Big Data systems Apache Hadoop and Spark. Focusing on the metric space approach relying on reference dataset objects (pivots), this paper investigates distributed similarity join techniques with and without approximation guarantees and also proposes high-dimensional extensions to previously proposed algorithms. The paper describes the design guidelines, algorithmic details, and key theoretical underpinnings of the compared approaches and also presents the empirical performance evaluation, approximation precision, and scalability properties of the implemented algorithms. Moreover, the Spark source code of all these algorithms has been made publicly available. Key findings of the experimental analysis are that randomly initialized pivot-based methods perform well with big high-dimensional data and that, in general, the selection of the best algorithm depends on the desired levels of approximation guarantee, precision and execution time. (C) 2019 Elsevier Ltd. All rights reserved.

Název v anglickém jazyce

Pivot-based approximate k-NN similarity joins for big high-dimensional data

Popis výsledku anglicky

Given an appropriate similarity model, the k-nearest neighbor similarity join represents a useful yet costly operator for data mining, data analysis and data exploration applications. The time to evaluate the operator depends on the size of datasets, data distribution and the dimensionality of data representations. For vast volumes of high-dimensional data, only distributed and approximate approaches make the joins practically feasible. In this paper, we investigate and evaluate the performance of multiple MapReduce-based approximate k-NN similarity join approaches on two leading Big Data systems Apache Hadoop and Spark. Focusing on the metric space approach relying on reference dataset objects (pivots), this paper investigates distributed similarity join techniques with and without approximation guarantees and also proposes high-dimensional extensions to previously proposed algorithms. The paper describes the design guidelines, algorithmic details, and key theoretical underpinnings of the compared approaches and also presents the empirical performance evaluation, approximation precision, and scalability properties of the implemented algorithms. Moreover, the Spark source code of all these algorithms has been made publicly available. Key findings of the experimental analysis are that randomly initialized pivot-based methods perform well with big high-dimensional data and that, in general, the selection of the best algorithm depends on the desired levels of approximation guarantee, precision and execution time. (C) 2019 Elsevier Ltd. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-22224S" target="_blank" >GA17-22224S: Analytika uživatelských preferencí v modelech multimediální explorace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Information Systems

ISSN

0306-4379

e-ISSN

—

Svazek periodika

87

Číslo periodika v rámci svazku

January 2020

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

101410

Kód UT WoS článku

000495488600006

EID výsledku v databázi Scopus

2-s2.0-85070216906