Scalability of visualization’s evaluation:

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.