Before revision:  As part of this evaluation we propose a novel classification scheme for time-dependent tree datasets. We approximate the infinite problem space of all such datasets by a low-dimensional feature space spanned by measurable characteristics (features) of the problem instances. We propose four such features to characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly.
After revision:  As part of this evaluation we propose a novel classification scheme for time-dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly.