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A Unified Indexing Strategy for the Mixed Data of a Future Marine GIS

Published online by Cambridge University Press:  06 February 2017

Tao Liu Open the ORCID record for Tao Liu [Opens in a new window] ,
Xie Han ,
Jie Yang  and
Liqun Kuang
Tao Liu*
Affiliation:
(School of Computer Science and Control Engineering, North University of China)
Xie Han
Affiliation:
(School of Computer Science and Control Engineering, North University of China)
Jie Yang
Affiliation:
(Transportation Management College, Dalian Maritime University)
Liqun Kuang
Affiliation:
(School of Computer Science and Control Engineering, North University of China)
*
(E-mail: [email protected])
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Abstract

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

Keywords

Spatial indexingData retrievalMixed dataMarine GIS
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 4 , July 2017 , pp. 735 - 747
Copyright
Copyright © The Royal Institute of Navigation 2017 

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