Efficient large graph pattern mining for big data in the cloud
Journal
Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages
531-536
Date Issued
2013
Author(s)
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. © 2013 IEEE.
Subjects
Big data; Cloud computing; Graph pattern mining
Other Subjects
Big data; Cloud computing; Graph algorithms; Graph mining; Graph partition problem; Graph patterns; High scalabilities; Large graphs; Massive graph; New approaches; Real data sets; Data mining
Type
conference paper