Scalability of visualization’s evaluation:

Muhammad Ghanbari
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引用次数: 1

Abstract

Information visualization has progressed and taken big steps in previous decade, despite challenging complexities of presenting and transforming the data. Visualization binds the perceptual capabilities of the human visual system. In the data, Human being looks for structure, pattern, features, anomalies, and relationship. Visualization, support this by preparing the data in a way to drive particular sense that differentiate various interactions and understanding. How human being receives and interacts with a visualization tools, can strongly influences his understanding of the data as well as the system's usefulness. Therefore, understanding the tools, relationships, and how well be able to depict the blue print of the model in mind, is not an easy task. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such a task [2]. We need better metrics and benchmark repositories to compare tools, and we should also seek reports of successful adoption and demonstrated utility. Moreover, there is a large range of target audience with different background and therefore, examining the concept, data, and analytic methodologies for these class of audience also is a big step in the right way. Furthermore, we also should consider how tools -for transformation and presentation - can improve mental activities of developer. This mental support has been defined as ";cognitive support"; [3]. So, are we able to explicitly state and compare claims about how particular tool support cognition? Are there capable theories for backdrop, onto which suitable theories and claims can be painted? Unfortunately, there are too many factors and relations which we should consider in order to be able to have a clear cut of measuring the relationships and their boundaries. In this paper, I'll try to open the question and shed on some important and very difficult aspect of visualization evaluation.
可视化评价的可扩展性:
信息可视化在过去十年中取得了很大的进步,尽管呈现和转换数据具有挑战性和复杂性。可视化结合了人类视觉系统的感知能力。在数据中,人类寻找结构、模式、特征、异常和关系。可视化,通过准备数据来支持这一点,以驱动区分各种交互和理解的特定意义。人类如何接收和交互可视化工具,可以强烈地影响他对数据的理解以及系统的有用性。因此,理解工具、关系,以及如何在头脑中描绘模型的蓝图,并不是一件容易的事。通常情况下,成功的决策和分析更多的是偶然发现和用户体验的问题,而不是对此类任务的有意设计和具体支持[2]。我们需要更好的度量和基准库来比较工具,我们还应该寻求成功采用和演示实用程序的报告。此外,有很大范围的目标受众具有不同的背景,因此,检查这些受众类别的概念,数据和分析方法也是正确的一大步。此外,我们还应该考虑用于转换和表示的工具如何改善开发人员的心理活动。这种心理支持被定义为“认知支持”;[3]. 那么,我们是否能够明确地陈述和比较关于特定工具如何支持认知的说法?是否有可行的理论作为背景,在其上可以描绘合适的理论和主张?不幸的是,有太多的因素和关系,我们应该考虑,以便能够有一个明确的衡量关系和他们的边界。在本文中,我将尝试打开这个问题,并阐述可视化评估的一些重要和非常困难的方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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