Efficient Index and Query Algorithm Based on Geospatial Big Data

被引:0
|
作者
Zhao, Huihui [1 ,2 ]
Zhao, Fan [2 ,3 ]
Chen, Renhai [1 ,2 ]
Feng, Zhiyong [1 ,2 ]
机构
[1] College of Intelligence and Computing, Tianjin University, Tianjin,300350, China
[2] Shenzhen Research Institute of Tianjin University, Shenzhen,Guangdong,518000, China
[3] Tianjin International Engineering Institute, Tianjin University, Tianjin,300350, China
关键词
Clustering algorithms;
D O I
10.7544/issn1000-1239.2020.20190565
中图分类号
学科分类号
摘要
In recent years, with the rapid development of advanced technologies such as intelligent target recognition, electronic sensors, collaborative control and computer networks, intelligent transportation systems have achieved qualitative leapfrogging. Modern intelligent transportation systems can realize intelligent transportation of vehicles, roads and clouds management platform. However, the intelligent transportation system relies on a large amount of two-dimensional geospatial information data generated every day. Therefore, how to efficiently store and query large-scale geospatial data is of great significance for the future popularization and development of the intelligent transportation system. However, due to the complexity of urban traffic information, large amount of data, and fast update speed, the current spatial indexing technology is difficult to efficiently search for two-dimensional geospatial information data. In order to optimize the storage organization structure of two-dimensional geospatial information data under spatial big data and improve retrieval efficiency, this paper proposes a spatial index tree construction algorithm for multi-layer slice recursion of two-dimensional geospatial information data (multi-layer slice recursive, MSR). The proposed algorithm first sorts and divides the first dimension of the map data to generate FD slices. Then, the second dimension of the map data in the FD slice is sorted to generate SD slices, and in the SD slice, the current slice and the adjacent slices are divided into spatial objects. Finally, data clustering operation is performed on the comparison between the length of the spatial object and the node capacity, and the MSR Tree is recursively generated from the bottom up by judging whether all the slices complete the clustering operation. Experimental results show that the query performance of the 2-dimensional space storage structure constructed by the MSR algorithm is better than the most representative spatial indexing technology based on the R-tree batch-loading algorithm (sort tile recursive, STR), STR-grid hybrid algorithm (str-grid), and efficient geometric range query (EGRQ). © 2020, Science Press. All right reserved.
引用
收藏
页码:333 / 345
相关论文
共 50 条
  • [1] Efficient Spark-Based Framework for Big Geospatial Data Query Processing and Analysis
    Aljawarneh, Isam Mashhour
    Bellavista, Paolo
    Corradi, Antonio
    Montanari, Rebecca
    Foschini, Luca
    Zanotti, Andrea
    [J]. 2017 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2017, : 851 - 856
  • [2] Efficient indexing RDF query algorithm for big data
    Zeng, Yiqun
    Wang, Jingbin
    [J]. MACHINERY ELECTRONICS AND CONTROL ENGINEERING III, 2014, 441 : 691 - 694
  • [3] Efficient Stereo Index Technology for Fast Combination Query of Electric Power Big Data
    Zeng Nan
    Hao Hanyong
    Zheng Haiyan
    [J]. 2016 FIRST IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND THE INTERNET (ICCCI 2016), 2016, : 329 - 333
  • [4] A Top-k Query Algorithm for Big Data Based on MapReduce
    Lin, Xueyan
    [J]. PROCEEDINGS OF 2015 6TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE, 2015, : 982 - 985
  • [5] Geospatial Big Data or Big Geospatial Data: A Bibliometric Review
    Ndu, Chidinma Godsgood
    Shoko, Moreblessings
    [J]. SOUTH AFRICAN JOURNAL OF GEOMATICS, 2024, 13 (01): : 158 - 171
  • [6] Efficient Geospatial Analytics on Time Series Big Data
    Al Jawameh, Isam Mashhour
    Bellavista, Paolo
    Corradi, Antonio
    Foschini, Luca
    Montanan, Rebecca
    [J]. IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 3002 - 3008
  • [7] Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data
    Engelinus, Jonathan
    Badard, Thierry
    Bernier, Eveline
    [J]. GEOGRAPHICAL INFORMATION SYSTEMS THEORY, APPLICATIONS AND MANAGEMENT, GISTAM 2018, 2019, 1061 : 133 - 148
  • [8] Ontology-Based Geospatial Data Query and Integration
    Zhao, Tian
    Zhang, Chuanrong
    Wei, Mingzhen
    Peng, Zhong-Ren
    [J]. GEOGRAPHIC INFORMATION SCIENCE, 2008, 5266 : 370 - +
  • [9] An Efficient Query Index on RFID Streaming Data
    Park, Jaekwan
    Hong, Bonghee
    Ban, Chaehoon
    [J]. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2009, 25 (03) : 921 - 935
  • [10] Efficient query processing platform for uncertain big data
    Huang, Zhenhua
    Zhang, Jiawen
    Fang, Qiang
    [J]. International Journal of Database Theory and Application, 2015, 8 (05): : 149 - 160