Parallel and Distributed Spatial Outlier Mining in Grid: Algorithm, Design and Application

Chen, Chongcheng; Lin, Jun; Wu, Xiaozhu; Wu, Jianwei

doi:10.1007/s10723-015-9326-y

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…A geographical knowledge service Cloud test‐bed (GeoKSCloud) is developed by our research team, which is an internet‐based, intelligent Cloud platform. GeoKSCloud provides the main functions of distributed spatial data mining and spatial decision‐support based on the SOA architecture and Cloud middleware Hadoop.…”

Section: Related Workmentioning

confidence: 99%

“…Typical modules include Cloud resource center, Cloud information center, execution management center, knowledge service center, and Cloud platform management center with a uniform Cloud portal interface. Moreover, GeoKSCloud provides a novel problem‐solving environment that will enable the in‐depth study of distributed D‐TIN algorithms in Cloud environment …”

Section: Related Workmentioning

confidence: 99%

“…Through years of research on spatial data-mining algorithms and the mechanisms of Cloud service interoperability, several main function modules have reached maturity and provide better and reliable distributed spatial knowledge services with map-reduce architecture.Typical modules include Cloud resource center, Cloud information center, execution management center, knowledge service center, and Cloud platform management center with a uniform Cloud portal interface. Moreover, GeoKSCloud provides a novel problem-solving environment that will enable the in-depth study of distributed D-TIN algorithms in Cloud environment 26.…”

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confidence: 99%

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Adaptive parallel Delaunay triangulation construction with dynamic pruned binary tree model in Cloud

Lin

Chen

et al. 2017

Concurrency and Computation

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Summary The paper illustrates a parallel and distributed scheme for computing a planar Delaunay triangulation using a divide‐and‐conquer strategy in Cloud environment, which combines the incremental insertion algorithm and the divide‐and‐conquer method. The proposed hybrid algorithm for Delaunay triangulation construction is easy to be parallelized due to the dynamic pruned characteristic of the binary tree model used. Moreover, the Cloud platform decreases the communication overhead and improves data locality by making use of a data partitioning and integrating scheme offered by the map‐reduce architecture. The implementation of the parallel and distributed version of the algorithm relied on a robust data structure called quad‐edge, which implies the geometric relationship among the edges and vertexes adjacent. More importantly, the data are serialized easily and transmitted efficiently between different Cloud nodes; the algorithm is executed conveniently on PC clusters. We tested the parallel version of the algorithm on GeoKSCloud, a geographical knowledge service Cloud developed by our research team. Experimental results show that the proposed hybrid algorithm is efficient and competitive; it can be easily migrated and deployed in distributed and parallel computing environment, such as grid and Cloud. The parallel implementation of the hybrid algorithm has a good speed‐up, while data communication is the crucial factor for the efficiency of the parallel version. Overall, the parallel version outperforms both the sequential divide‐and‐conquer algorithm and the sequential incremental insertion algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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