Quantitative Comparison of Time-Dependent Treemaps
| Autor: | Bettina Speckmann, Max Sondag, Eduardo Faccin Vernier, João Luiz Dihl Comba, Kevin Verbeek, Alexandru Telea |
|---|---|
| Přispěvatelé: | Applied Geometric Algorithms, Algorithms, Geometry and Applications, EAISI Foundational, EAISI Health, Scientific Visualization and Computer Graphics |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computational Geometry (cs.CG)
FOS: Computer and information sciences CCS Concepts Computer science media_common.quotation_subject • Human-centered computing → Treemaps • Information systems → Temporal data Stability (learning theory) 020207 software engineering 02 engineering and technology computer.software_genre Computer Graphics and Computer-Aided Design Human-centered computing Temporal database Variety (cybernetics) 0202 electrical engineering electronic engineering information engineering Information system Computer Science - Computational Geometry 020201 artificial intelligence & image processing Quality (business) Data mining Treemapping computer media_common |
| Zdroj: | Computer Graphics Forum, 39(3), 393-404. Wiley-Blackwell COMPUTER GRAPHICS FORUM, 39(3), 393-404. Wiley |
| ISSN: | 0167-7055 |
| Popis: | Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps that can handle time-dependent data, and (b) corresponding quality criteria that cover both a treemap's visual quality and its stability over time. In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations. We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end, we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data. 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. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms. All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies. |
| Databáze: | OpenAIRE |
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