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Title: A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edges in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.
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Conference: IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV 2015), October 25-26, 2015, Chicago, Illinois, 57-64
Institute of Electrical and Electronics Engineers, Piscataway, NJ, United States(US).
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Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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Country of Publication:
United States
Visual analytics; graph visualization; web-scale graph; NetFlow; triple-store; RDF; SPARQL; high-performance computing; Linux cluster