Temporal and Spatial Change Detection for Scientific Data Set Stream
Identifying “important time frames and subregions from massive scientific data set stream and directing scientists to pay attention to segments of interest in the data is an important research area. We introduced a general approach for change detection which based on statistical technology and designed algorithms to reveal important time-frames and subregions from scientific data set stream. Experiment Results obtained with synthetic data and plasma simulation data are presented. Our work can remarkably improve the way in which scientists extract useful information from large, complex, scientific data.
Guoqing Wu Hong Chen Liqiang Cao
Institute of Applied Physics and Computing Mathematics, Beijing, 100094, China
国际会议
上海
英文
618-622
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）