标题：SpatialGraphx: A Distributed Graph Computing Framework for Spatial and Temporal Data at Scale
作者：Wang, Changyuan; Lu, Zongfei; Liu, Yang; Guo, Shanqing; Xu, Xinshun; Liu, Shijun; Cui, Lizhen
作者机构：[Wang, Changyuan; Lu, Zongfei; Liu, Yang; Guo, Shanqing; Xu, Xinshun; Liu, Shijun; Cui, Lizhen] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples 更多
会议名称：40th Annual IEEE Computer Software and Applications Conference Symposium (COMPSAC)
会议日期：JUN 10-14, 2016
来源：PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS, VOL 1
关键词：partial graph computations; spatio-temporal index; Location-based; partition strategy
摘要：Development of the Smart City has produced much data with attributions of timestamp and location, but in some applications like investigation of the large bomb explosion in New York, the government takes precedence to investigate the relation data from New York city rather than the whole Country, which prompts us to do some research works in computing partial graph more fast. So we propose SpatialGraphx, a graph parallel computing framework supporting direct and fast partial graph construction and partial graph computation. Leveraging the spatial and temporal attributions of data, SpatialGraphx presents two extensions on the partial graph construction by building a spatio-temporal tree index and on the computation by a new location-based partition strategy. Using mobile network's data with hundred million edges, we demonstrate SpatialGraphx can support direct and fast partial graph construction and enables efficient partial graph analysis for spatial and temporal data. And compared to original Graphx, the improvement of SpatialGraphx is 3x to several orders of magnitude for large enough dataset.