In recent years a common trend characterised by the adoption of text mining methods for the study of digital sources emerged in digital humanities, often in opposition to traditional hermeneutic approaches. In our paper, we intend to show how text mining methods will always need a strong support from the humanist. On the one hand we remark how humanities research involving computational techniques should be thought of as a three steps process: from close reading (identification of a specific case study, initial feature selection) to distant reading (text mining analysis) to close reading again (evaluation of the results, interpretation, use of the results). Moreover, we highlight how failing to understand the importance of all the three steps is a major cause for the mistrust in text mining techniques developed around the humanities. On the other hand we observe that text mining techniques could be a very promising tool for the humanities and that researchers should not renounce to such approaches, but should instead experiment with advanced methods such as the ones belonging to the family of deep learning. In this sense we remark that, especially in the field of digital humanities, exploiting complementarity between computational methods and humans will be the most advantageous research direction.

(a) Approximated GVD (b) Filtered grid (c) Multiple tiles (d) ECM navigation mesh Figure 1: The pipeline of our algorithm. (a) For a set of obstacles (a 'U' and a bounding box), we approximate the Generalized Voronoi Diagram (GVD) using the GPU framebuffer. (b) Close-up. Instead of copying the entire framebuffer to the CPU, we copy only the grid points that are relevant to the GVD (white disks). (c) By subdividing large buffers into multiple tiles, we lift the algorithm to virtually infinite resolutions. (d) The ECM navigation mesh is obtained by marking event points (small dots) where obstacle normals are intersected, and adding closest obstacle points (gray segments) to event points and edge endpoints. We compute most of this data on the GPU.

The pipeline of our algorithm. (a) For a set of obstacles (a 'U' and a bounding box), we approximate the Generalized Voronoi Diagram (GVD) using the GPU framebuffer. (b) Close-up. Instead of copying the entire framebuffer to the CPU, we copy only the grid points that are relevant to the GVD (white disks). (c) By subdividing large buffers into multiple tiles, we lift the algorithm to virtually infinite resolutions. (d) The ECM navigation mesh is obtained by marking event points (small dots) where obstacle normals are intersected, and adding closest obstacle points (gray segments) to event points and edge endpoints. We compute most of this data on the GPU. AbstractThis paper presents a GPU-accelerated approach for improving the approximated construction of Generalized Voronoi Diagrams (GVDs). Previous work has shown how to render a GVD onto the GPU framebuffer, and copy it to the CPU for extraction of a high-quality diagram. We improve upon this technique by performing more computations in parallel on the GPU, and reducing the amount of data transferred to the CPU. We also design a multi-tiled construction technique that overcomes hardware limitations and enables much higher rendering resolutions, thus reducing discretization errors. Next, we extend our approach to create an Explicit Corridor Map navigation mesh, which is an efficient data structure for path planning in modern crowd simulation systems. The new implementation allows much faster construction of GVDs and navigation meshes at virtually infinite resolutions.

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