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2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) (2016)
Atlanta, GA, USA
June 10, 2016 to June 14, 2016
ISSN: 0730-3157
ISBN: 978-1-4673-8846-7
pp: 608-613
ABSTRACT
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.
INDEX TERMS
Spatial databases, Distributed databases, Vegetation, Buildings, Indexing, Spatial indexes
CITATION

C. Wang et al., "SpatialGraphx: A Distributed Graph Computing Framework for Spatial and Temporal Data at Scale," 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, GA, USA, 2016, pp. 608-613.
doi:10.1109/COMPSAC.2016.195
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