The rapid advance in high performance computing and measurement technologies has recently made it possible to produce a stupendous amount of time-varying volume datasets in a variety of disciplines. However, there exist a few known visual exploration tools that allow us to investigate the core of their complex dynamics effectively. In this paper, our previous approach to topological volume skeletonization is extended to capture the topological features of large-scale time-varying volume datasets. A visual exploration tool, termed T-map, is presented, where pixel-oriented information visualization techniques are deployed so that the user can identify partial 4D spatiotemporal domains with characteristic changes in a topological sense, prior to detailed and comprehensible volume visualization. A case study with datasets from atomic collision research is performed to illustrate the feasibility of the proposed tool.
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