cloud computing, parallel algorithms, graph data analysis, data mining, social network analysis
The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel (BSP) computing model named BSP-based Parallel Graph Mining (BPGM). This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.
Tsinghua University Press
Yang Liu, Bin Wu, Hongxu Wang et al. BPGM: A Big Graph Mining Tool. Tsinghua Science and Technology 2014, 19(1): 33-38.