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
Em termos de agregação espacial online, os métodos seriais independentes tradicionais tornam-se gradualmente limitados. Embora a computação paralela seja amplamente estudada hoje em dia, poucas pesquisas foram realizadas sobre métodos de agregação online paralelos baseados em índices, especificamente para dados espaciais. Nesta carta, é proposto um método paralelo de indexação multinível para acelerar as análises de agregação espacial online, que contém duas etapas. Na primeira etapa, um índice de árvore AR paralelo é construído para acelerar a consulta agregada localmente. Na segunda etapa, uma estrutura de pirâmide de dados de amostragem multinível é construída com base no índice de árvore AR paralelo, que contribui para os resultados da consulta retornados simultaneamente com certo grau de confiança. Resultados experimentais e analíticos verificam que os métodos são capazes de lidar com dados em escala de bilhões.
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 -