Adaptive visualization for analyzing large-scale network datasets

Miyamura, Hiroko  ; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*

In this paper, we propose a visualization technique for handling large-scale experiment datasets in distributed file system. Traditional network visualization techniques have problem that not all data can be displayed at the same time because the file-sharing network data is too large-scale. Therefore, we propose a concept of adaptive network graph display, in which the graph style can be changed according to the details of user's observation. We construct a visualization system based on this concept. When observing data globally, the proposed system selectively displays information based on the clustering result, and when observing data locally, the system displays detailed information. This technique is a basic technology for computational science to achieve large-scale datasets handling which is a problem in atomic energy related fields.