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Método de índice de grade fusionada para consultas espaço-temporais de big data de sensoriamento remoto

  • role： First author第一作者
  • Affiliation：

    Institute of Software of Chinese Academy of Sciences, Beijing 100190, China

    University of Chinese Academy of Sciences, Nanjing 211135, China

    Nanjing Institute of Software Technology, Nanjing 210000, China

  • Email：chenjunzhe21@otcaix.iscas.ac.cn
  • Introduction：陈俊哲，研究方向为分布式软件理论与技术。E-mail： chenjunzhe21@otcaix.iscas.ac.cn
CHEN Junzhe123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Software of Chinese Academy of Sciences, Beijing 100190, China

    University of Chinese Academy of Sciences, Nanjing 211135, China

    Nanjing Institute of Software Technology, Nanjing 210000, China

  • Email：ljie@otcaix.iscas.ac.cn
  • Introduction：刘杰，研究方向为地球大数据、分布式系统。E-mail： ljie@otcaix.iscas.ac.cn
LIU Jie123*,  
  • Affiliation：

    The International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100094, China

QIN Yuchu45,  
  • Affiliation：

    Nanjing Institute of Software Technology, Nanjing 210000, China

CAO Yu3,  
  • Affiliation：

    Institute of Software of Chinese Academy of Sciences, Beijing 100190, China

    Nanjing Institute of Software Technology, Nanjing 210000, China

WANG Shuai13,  
  • Affiliation：

    Institute of Software of Chinese Academy of Sciences, Beijing 100190, China

YE Dan1,  
  • Affiliation：

    Institute of Software of Chinese Academy of Sciences, Beijing 100190, China

ZHONG Hua1

Resumo

O sistema de processamento online de big data de sensoriamento remoto suportado por plataforma em nuvem fornece suporte eficiente para aplicações inovadoras em múltiplos campos de imagens de sensoriamento remoto. No entanto, os sistemas de banco de dados existentes respondem lentamente às consultas espaço-temporais de metadados de milhões de imagens, dificultando o atendimento dos requisitos temporais do sistema de processamento online. Consultas espaço-temporais complexas e de grande escala tornaram-se um gargalo de desempenho na análise de imagens de sensoriamento remoto. Este artigo propõe um método eficiente de indexação e otimização de consultas espaço-temporais que integra a divisão da grade bidimensional e tridimensional. O método inicialmente realiza o pareamento do intervalo da consulta com base na divisão da grade bidimensional e, a partir daí, combinando a condição temporal, converte algumas células da grade em células tridimensionais para melhorar a eficiência da execução da consulta. As células globais da grade são construídas recursivamente e a codificação Z-order é usada para mapear as células da grade em índices inteiros unidimensionais, convertendo consultas espaço-temporais em consultas de intervalo de codificação. Ao mesmo tempo, é projetado um mecanismo de particionamento de consulta baseado na localidade espaço-temporal da grade para suportar a execução paralela de tarefas de consulta. O método foi implementado no MongoDB e PostGIS, com experimentos comparativos de desempenho realizados contra índices nativos do sistema, reduzindo o tempo de consulta em 93,11% e 88,02%, respectivamente. Atualmente, esta solução tecnológica foi integrada ao sistema EarthDataMiner da plataforma de big data SDG, lançado pelo Centro Internacional de Pesquisa em Big Data para o Desenvolvimento Sustentável, e está em serviço.

Palavras-chave

big data de sensoriamento remoto; imagens de sensoriamento remoto; consultas espaço-temporais; divisão de grade espaço-temporal; otimização de consultas; otimização de índices; plataforma de computação em nuvem para sensoriamento remoto; banco de dados espaço-temporal

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