Spatial Index Structure

2011 ◽  
Keyword(s):  
Spatial Index ◽  
Index Structure

scholarly journals Efficient Geo-Textual Hybrid Indexing Techniques for Moving Objects and Queries

2020 ◽  
Vol 8 (6) ◽  
pp. 4419-4428
Keyword(s):  
Moving Objects ◽  
Traffic Monitoring ◽  
Spatial Index ◽  
Index Structure ◽  
Maintenance Cost ◽  
Hybrid Index ◽  
Indexing Techniques ◽  
Inverted List ◽  
Textual Data ◽  
Future Position

Advancements of various Geographic Information Technologies have resulted in huge growth in Geo-Textual data. Many Indexing and searching algorithms are developed to handle this Geo-Textual data which contains spatial, textual and temporal information. In past, Indexing and searching algorithms are developed for the applications in which the object trajectory or velocity vector is known in advance and hence we can predict the future position of the objects. There are real time applications like emergency management systems, traffic monitoring, where the objects movements are unpredictable and hence future position of the objects cannot be predicted. Techniques are required to answer the geo-textual kNN query where the velocity vectors or trajectories of moving and moving queries are not known. In case of moving objects, capturing current position of the object and maintaining spatial index optimally is very much essential. The hybrid indexing techniques used earlier are based on R-tree spatial index. The nodes of the R-tree index structure are split or merged to maintain the locations of continuously moving objects, increasing the maintenance cost as compared to the grid index. In this paper a solution is proposed for creating and maintaining hybrid index for moving objects and queries based on grid and inverted list hybrid indexing techniques. The method is also proposed for finding Geo-Textual nearest neighbours for static and moving queries using hybrid index and conceptual partitioning of the grid. The overall gain reported by the experimental work using hybrid index over the non- hybrid index is 30 to 40 percent depending on the grid size chosen for mapping the data space and on the parameters of queries.

A Mobile Storage System for Massive Spatial Data

2014 ◽  
Vol 962-965 ◽  
pp. 2730-2734
Author(s):  
Zhen Song ◽  
Jian Chen ◽  
Jiu Yan Ye
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Spatial Data ◽  
Storage System ◽  
Hierarchical Classification ◽  
Spatial Index ◽  
Index Structure ◽  
Real Time Visualization ◽  
Spatial Data Organization ◽  
Low Efficiency

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

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