Research On Isocontours Extraction Of Large Scale Datasets Based On Span Space Partition

Visualization has been played an important role for analyzing and processing large-scale datasets. However, visualizing large-scale datasets still suffers lots of difficulties, such as preprocessing time is too long, rendering time is too long or consuming too much space. How to realize the real-time and interactive visualization of large scale dataset has become a hot topic of visualization.In this paper, we will discuss some large-scale datasets visualization methods. Aiming at the problems of current large-scale datasets visualization, we make use of quad tree based adaptive partition and bounding box based node construction methods to improve traditional binary interval tree and BBIO tree. The theory analysis and experiments show that our approaches are much efficient for large-scale datasets visualization. The contributions and relevant work in the paper are as follows.Firstly, we introduce an adaptive quad tree partition method for binary interval tree. In large-scale datasets visualization process, fat nodes will block the pre-processing efficiency and make visualization time uncontrollable. We use adaptive quad tree partition strategy to reform the construction of binary interval tree, and it not only reduce pre-processing time but also maintain the efficiency of searching active meta-cell. The experiments demonstrate that the adaptive quad tree method is faster than the traditional construction method, and will reduce the pre-processing time almost 50%, the difference between two methods in active meta-cell searching is only less 0.2s;Secondly, we propose a BBIO tree node construction method based on bounding box. We improve the traditional low efficiency of BBIO tree construction, and leverage Span Space bounding box to reform the layout of BBIO tree nodes, the experiments proved that our algorithm is more efficient than the traditional and will achieve 20% speed up for detecting active meta-cell;Finally,for solving the problems of large-scale dataset visualization, we have combined all approaches which is proposed in this paper, and integrated all techniques to a large-scale datasets isosurface extraction framework. We defined all interfaces and model functions well-formed and design a scalable, reusable visualization frame work. At last, we analysis the framework in modern software engineering angle, and in abstract level, we complete all integration work for our framework, and have built an excellent large-scale datasets visualization software framework.