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- W2026694064 abstract "Mining big graph data is an important problem in the graph mining research area. Although cloud computing is effective at solving traditional algorithm problems, mining frequent patterns of a massive graph with cloud computing still faces the three challenges: 1) the graph partition problem, 2) asymmetry of information, and 3) pattern-preservation merging. Therefore, this paper presents a new approach, the cloud-based SpiderMine (c-SpiderMine), which exploits cloud computing to process the mining of large patterns on big graph data. The proposed method addresses the above issues for implementing a big graph data mining algorithm in the cloud. We conduct the experiments with three real data sets, and the experimental results demonstrate that c-SpiderMine can significantly reduce execution time with high scalability in dealing with big data in the cloud." @default.
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- W2026694064 date "2013-10-01" @default.
- W2026694064 modified "2023-10-01" @default.
- W2026694064 title "Efficient large graph pattern mining for big data in the cloud" @default.
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- W2026694064 doi "https://doi.org/10.1109/bigdata.2013.6691618" @default.
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