Increasing amounts of large, dynamic, heterogeneous, distributed, and complex data raise the question how applications, systems, and users can benefit from this data. Faced with Big Data, most visualization techniques cannot maintain interactive frame rates, produce visual clutter, and are bound to limited memory. Therefore, in this master's thesis aggregation strategies and level-of-detail concepts are developed, evaluated, and integrated, enabling those techniques to handle massive amounts of data. For this, a high-performance hierarchy visualization and rendering framework is provided.
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