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Human-Centric Situational Awareness and Big Data Visualization

机译:以人为本的情境感知和大数据可视化

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Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on force-directed model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time.
机译:需要以有效的方式分析大数据所需的人为态势感知和可视化。其中一个挑战是创建一种算法来分析给定数据而没有其他数据分析工具的任何帮助。这项研究旨在确定根据分析师的心理模型开发的图形对象(如数据形状)如何提升分析师的情况意识。我们改善大数据可视化的方法是两倍,重点关注可视化和交互。本文介绍了基于3D力定向模型图的开发数据和图形技术。它是使用Unity 3D游戏引擎开发的。使用不同的数据集进行导频测试,用于检查沉浸环境中系统的效率和非沉浸式环境。应用程序能够成功地处理数据可视化中给定数据集的数据。当前图可以实时呈现左右200到300个链接节点。

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