大量的数据是通过Internet使用，环境中越来越多的传感器以及科学研究（例如大规模仿真）创建的。在许多领域，挑战已从生成足够数量的数据转移到了理解和使用它。数据可视化以最能产生洞察力和支持决策的方式呈现数据，即使在计算科学向万亿级存储迈进的过程中，新设备也通过“物联网”增加了数据海啸。在这些进步中，管理规模和复杂性可视化过程可能令人生畏。<br> <br>开发可视化过程需要创建数据的易于处理的表示形式，然后以交互方式操作和查询数据。研究人员通常必须使用户能够遍历从TB到PB的数据集。为了设计可视化，研究
Massive amounts of data are created by Internet use, an expanding number of sensors in the environment, and scientific research such as large-scale simulations. In many fields, the challenge has shifted from generating sufficient amounts of data to understanding and using it. Data visualization presents data in ways that best yield insight and support decisions- even as computational science pushes toward exascale capacity and new devices add to the data tsunami via the ''Internet of Things.'' Amid these advances, managing the scale and complexity of the visualization process can be daunting.<br><br>Developing visualizations requires creating a tractable representation of the data, then interactively manipulating and querying it. Often researchers must enable users to traverse data sets ranging from terabytes to petabytes. To design visualizations, researchers combine techniques from several disciplines, including data mining, machine learning, and human-computer interaction.<br><br>Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable.<br><br>CSE faculty Polo Chau and Jimeng Sun are part of the interdisciplinary GT Visualization (Vis) Group. Research areas of the larger GT Vis Group include information visualization, visual communication and design, data and visual analytics, and visualization in fields of business, journalism, humanities, and education.​