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We present a new framework that combines the hierarchical multi-resolution representation with video-based compression to manage and render large scale time-varying data. In the preprocessing step, the proposed method first constructs a multi-resolution hierarchy using octree structure for each individual time step, and then applies a motion-compensation-based prediction to compress the octree nodes. During rendering stage, the data is decompressed on-the-fly and rendered using hardware texture mapping. The proposed approach eliminates the hierarchical decompression dependency commonly found in the conventional hierarchical wavelet representation methods, which leads to a more efficient reconstruction of data along the time axis. The system provides the user with a spatial region-of-interest (ROI) to adjust the spatial level-of-detail (LOD) selection, and a temporal ROI which is a sub-region only for frequent update during playback. With a suitable control of both ROIs, our system can reach an interactive playback frame rate. This allows the user to observe the dynamic nature of large time-varying data sets.