Title of article :
BPGM: A Big Graph Mining Tool
Author/Authors :
Liu, Yang Beijing University of Posts and Telecommunications - School of Computer Science, China , Wu, Bin Beijing University of Posts and Telecommunications - School of Computer Science, China , Wang, Hongxu Beijing University of Posts and Telecommunications - School of Computer Science, China , Ma, Pengjiang Beijing University of Posts and Telecommunications - School of Computer Science, China
Abstract :
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.
Keywords :
cloud computing , parallel algorithms , graph data analysis , data mining , social network analysis
Journal title :
Tsinghua Science and Technology
Journal title :
Tsinghua Science and Technology
