ViQueL: A Spatial Query Language for Presentation-Oriented Documents

2010 ◽  
Author(s):  
Ermelinda Ora ◽  
Francesco Riccetti ◽  
Massimo Ruffolo
Keyword(s):  
Query Language ◽  
Spatial Query ◽  
Spatial Query Language

scholarly journals A SPARK BASED COMPUTING FRAMEWORK FOR SPATIAL DATA

2017 ◽  
Vol IV-4/W2 ◽  
pp. 125-130
Author(s):  
F. Xiao
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Query Language ◽  
Spatial Query ◽  
Data Types ◽  
Spatial Join ◽  
Large Size ◽  
Spatial Query Language ◽  
Spatial Indexes ◽  
User Friendly

In this paper, a novel Apache Spark-based framework for spatial data processing is proposed, which includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbour and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks based on Spark, it is highlighted that spatial indexes like grid, R-tree are used for data storage and query. Extensive experiments on real system prototype and real datasets show that better performance can be achieved.

scholarly journals A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

2018 ◽  
Vol XLII-3 ◽  
pp. 1945-1950 ◽  
Author(s):  
F. Xiao
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Spatial Data ◽  
Query Language ◽  
Spatial Query ◽  
K Nearest Neighbor ◽  
Data Types ◽  
National Geographic ◽  
Spatial Query Language ◽  
Spatial Data Processing

In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

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