The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
En termes d’agrégation spatiale en ligne, les méthodes traditionnelles autonomes en série deviennent progressivement limitées. Bien que le calcul parallèle soit largement étudié de nos jours, peu de recherches ont été menées sur les méthodes d’agrégation parallèle en ligne basées sur des index, en particulier pour les données spatiales. Dans cette lettre, une méthode d'indexation multiniveau parallèle est proposée pour accélérer les analyses d'agrégation spatiale en ligne, qui comprend deux étapes. Dans la première étape, un index d’arborescence aR parallèle est construit pour accélérer localement les requêtes agrégées. Dans la deuxième étape, une structure pyramidale de données d'échantillonnage multiniveau est construite sur la base de l'index d'arborescence aR parallèle, qui contribue aux résultats de requête renvoyés simultanément avec un certain degré de confiance. Les résultats expérimentaux et analytiques vérifient que les méthodes sont capables de traiter des données à l’échelle d’un milliard.
Luo CHEN
National University of Defense Technology
Ye WU
National University of Defense Technology
Wei XIONG
National University of Defense Technology
Ning JING
National University of Defense Technology
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Luo CHEN, Ye WU, Wei XIONG, Ning JING, "A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3242-3245, December 2018, doi: 10.1587/transinf.2018EDL8120.
Abstract: In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8120/_p
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@ARTICLE{e101-d_12_3242,
author={Luo CHEN, Ye WU, Wei XIONG, Ning JING, },
journal={IEICE TRANSACTIONS on Information},
title={A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis},
year={2018},
volume={E101-D},
number={12},
pages={3242-3245},
abstract={In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.},
keywords={},
doi={10.1587/transinf.2018EDL8120},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 3242
EP - 3245
AU - Luo CHEN
AU - Ye WU
AU - Wei XIONG
AU - Ning JING
PY - 2018
DO - 10.1587/transinf.2018EDL8120
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.
ER -