China
Africa
Explore chapters and articles related to this topic
Big Geospatial Data and the Geospatial Semantic Web: Current State and Future Opportunities
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Chuanrong Zhang, Weidong Li, Tian Zhao
Queries over the GSW may require joining two or more types of spatial objects. In spatial databases, spatial join algorithms are used to improve runtime performance and the same algorithms can also be applied to improve query performance over the GSW. The choice of these algorithms depends on whether one or more spatial indices are present.
A Review of Spatial Big Data Platforms, Opportunities, and Challenges
Published in IETE Journal of Education, 2020
The Object Relational DBMS (ORDBMS), an object-oriented extension of RDBMS emerged in the 1990s [13]. The Abstract Data Type (ADT) feature of ORDBMS was leveraged to build spatial data types and operations. This resulted in Spatial Database Management System (SDBMS) that provides native support for efficient storage and querying of spatial data. The SDBMS is thus an extended version of ORDBMS that supports storage and querying of spatial data. It supports spatial data model, spatial query language, spatial indexing, and spatial operations. It can import shapefile from Desktop GIS into centralized native spatial data storage. The point, multipoint, line, multiline, polygon, polygon with hole, multi-polygon, and geometry collection spatial data types are supported by SDBMS. The spatial join operations like intersects, contains, covers, crosses, and within can join different spatial entities based on their spatial relationships. The way B-Tree access method is used in RDBMS to improve query performance; R-tree and GiST are spatial access methods for indexing points, lines, and polygons in SDBMS [14]. A brief comparison of RDBMS and SDBMS for spatial data is shown in Table 1.