Abstract
Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time.
Highlights
The rapid and continuous growth of geospatial information generated from devices such as smartphones, satellites, and other Internet of Things (IoT) devices means that traditional Geographic Information System (GIS) cannot support such a large amount of data [1,2]
We presented the 2DPR-Tree as a new partitioning technique in SpatialHadoop
Quadtree performed best for the range queries with small query window areas equal to 0.01% and 1% of the input dataset area
Summary
The rapid and continuous growth of geospatial information generated from devices such as smartphones, satellites, and other Internet of Things (IoT) devices means that traditional Geographic Information System (GIS) cannot support such a large amount of data [1,2]. GIS is insufficient in this situation because of poor adaptability of the basic incorporated frameworks Blending both GIS and cloud computing represents a new era for the advancement of data storage and processing, and their applications in GIS [3,4]. A primary inadequacy is the absence of any indexing mechanism that could support specific access to spatial information in particular areas due to the demands for effective query processing. Because of this issue, an expansion of Hadoop, called SpatialHadoop, has been developed.
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