eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

Zhifei Ding, Jiahao Han, Rongtao Qian, Liming Shen, Siru Chen, Lingxin Yu, Yu Zhu, Richen Liu

Abstract: We propose eBoF, a novel time-varying ensemble data visualization approach based on the Bag-of-Features (BoF) model. In the eBoF model, we extract a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch. Each local feature of a semantically simple single interval can be defined as a feature patch within the BoF model, with the duration of each interval (i.e., feature patch) serving as its frequency. Feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates clusters along with their probability distributions across all feature patches while preserving the geo-spatial information, which is often lost in traditional topic modeling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results, evaluated by domain knowledge. We conduct case studies and performance tests to evaluate the eBoF model and gather feedback from domain experts to further refine it. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.