Chen, C.-C.C.-C.ChenLee, K.-W.K.-W.LeeChang, C.-C.C.-C.ChangYang, D.-N.D.-N.YangMING-SYAN CHEN2018-09-102018-09-102013http://www.scopus.com/inward/record.url?eid=2-s2.0-84893271959&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/379786Mining 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.Big data; Cloud computing; Graph pattern miningBig data; Cloud computing; Graph algorithms; Graph mining; Graph partition problem; Graph patterns; High scalabilities; Large graphs; Massive graph; New approaches; Real data sets; Data miningconference paper10.1109/BigData.2013.66916182-s2.0-84893271959