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Evaluating Techniques for Interactive Exploration and Visualization of Large Astronomical Datasets

机译:评估大天文数据集的交互式探索与可视化技术

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As large surveys of hundreds millions of objects are common, helping users locating their data subset of interest through interactive exploration and visualiza-tion is becoming a challenge of major concern. In this paper, we present two prototypes we developed to tackle this issue. Using Datavore and D3, we developed a pure-Javascript SPLOM (scatter plot matrix) visualizer taking a VOTable as an input. Linked views allow one to distinguish correlations between displayed attributes. This approach works well until 50k-100k objects, but does not scale beyond because of browsers limitations. For larger datasets, we adapted the Nanocubes datastructure initially created by AT&T Research for interactive visualization of spatial-temporal datasets. Our version, developed in Java, allows fast interactive visualization of a catalogue with hundred million rows for a few attributes. HiPS (Hierarchical Progressive Surveys) heatmaps are dynamically generated according to selected criteria and displayed in Aladin Lite. Eventually, we will discuss the benefits and limitations of these approaches, ex-plore possible improvements and describe how these techniques might be integrated in existing CDS services.
机译:随着数百百万物体的大量调查很常见,帮助用户通过交互式探索找到他们的数据对感兴趣的兴趣子集和Visualiza-Tion正在成为主要问题的挑战。在本文中,我们提出了两个我们开发的原型来解决这个问题。使用DataVore和D3,我们开发了一种纯-JavaScript SPLOM(散点图矩阵)可视化器,可作为输入。链接视图允许一个区分显示属性之间的相关性。这种方法效果很好,直到50k-100k对象,但由于浏览器限制而不会扩展。对于较大的数据集,我们调整了最初由AT&T研究空间 - 时间数据集的交互式可视化的AT&T研究纳米尺度数据结构。我们的版本在Java中开发,允许少数几个属性的亿行快速交互式可视化。臀部(分层渐进式调查)根据所选择的标准动态生成HIPS(分层渐进式调查),并在Aladin Lite中显示。最终,我们将讨论这些方法的好处和限制,例如,可能的改进,并描述了如何在现有的CDS服务中集成这些技术。

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