Spatial data integration for the environmental characterization of pasture macrotypes in the Italian Alps

2015 ◽  
Vol 71 (2) ◽  
pp. 219-234 ◽  
Author(s):  
C. Dibari ◽  
M. Bindi ◽  
M. Moriondo ◽  
N. Staglianò ◽  
S. Targetti ◽  
...  
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Italian Alps ◽  
Environmental Characterization ◽  
Spatial Data Integration

Spatial Data Integration

2019 ◽  
pp. 1539-1546 ◽  
Author(s):  
Booma Sowkarthiga Balasubramani ◽  
Isabel F. Cruz
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Spatial Data Integration

Optimized Spatial Query for Data Integration Based on Semantics

2012 ◽  
Vol 229-231 ◽  
pp. 1895-1899
Author(s):  
Shen Yi Qian ◽  
Hao Dong Zhu
Keyword(s):  
Data Integration ◽  
Query Optimization ◽  
Spatial Data ◽  
Hybrid Approach ◽  
Detailed Examination ◽  
Spatial Knowledge ◽  
Spatial Query ◽  
Spatial Queries ◽  
Spatial Data Integration ◽  
Spatial Semantics

Data integration of geospatial data in distributed, heterogeneous environment involves the use of semantic ontologies. In this kind of integration system, semantic technologies play an important role in improving performance and effectiveness of spatial queries. This paper focuses on methods of query optimization based on spatial semantics at the top level of semantic layer in central data integration systems. After analyzing the hybrid approach for spatial data integration, two categories of query optimization strategies are proposed based on detailed examination of special characteristics of spatial data. With spatial knowledge explicitly specified in ontologies and associated rules, spatial queries can be optimized intelligently.

scholarly journals A Multi-Scale Settlement Matching Algorithm Based on ARG

2016 ◽  
Vol XLI-B2 ◽  
pp. 139-143
Author(s):  
Han Yue ◽  
Xinyan Zhu ◽  
Di Chen ◽  
Lingjia Liu
Keyword(s):  
Data Integration ◽  
Change Detection ◽  
Spatial Data ◽  
Road Network ◽  
Small Scale ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Small Scales ◽  
Spatial Data Integration ◽  
Attributed Relational Graph

Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG) is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.

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