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Spatial queries include spatial selection query, spatial join query, nearest neighbor query, etc. Most of spatial queries are computing intensive and individual query evaluation may take minutes or even hours. Parallelization seems a good solution for such problems. However, parallel programs must communicate efficiently, balance work across all nodes, and(More)
MapReduce is a widely used parallel programming model and computing platform. With MapReduce, it is very easy to develop scalable parallel programs to process data-intensive applications on clusters of commodity machines. However, it does not directly support heterogeneous related data sets processing, which is common in operations like spatial joins. This(More)
MapReduce is a partition-based parallel programming model and framework enabling easy development of scalable parallel programs on clusters of commodity machines. In order to make time-intensive applications benefit from MapReduce on small scale clusters, this paper proposes a new method to improve the performance of MapReduce by using distributed memory(More)
Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of(More)
Traditionally, vector data management in geographic information system (GIS) applications often relies on general purposed database systems. However, the strict ACID semantics kept by database systems are not always necessary for most GIS applications to handle data in read-only mode. With the increasing quantity of vector data, the semantics mismatch has(More)
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