## 3 Method  
### UGVA Method and the Image Structure  
The methodology of system analysis implies that while analyzing complex, multiparameter systems, the analysis methods should allow for combining both the whole and its parts in their dynamics into a single entity [25]. Therefore, the developed method of cognitive visualization must follow the principles of context dependency, complexity, clarity (expressivity), and visual harmony of the image (balance). As with every method of data mining, it should not require the decision maker to learn complex methods to interpret the image. The Unified Graphic Visualization of Activity (UGVA) method is a method of visual representation of a profile of complex multiparameter system as an anthropomorphic image in order to demonstrate specifics of its functional (activity) and structural parameters and also to compare such profiles [26]. It is based on the image of a human-like body, which is synthesized and displayed according to raw or generalized values of the key parameters of the analyzed system and can be overlaid with data necessary to make different decisions. An image in the UGVA notation is structured along the set of axes mapping key parameters (criteria) \(S_{p}\) or their groups (normally, these zones are in the limbs of the figure), zone of integral assessment of the system's state (as a rule, the emotion on the face), coloring of the image elements, and additional artifacts. Each zone of the image is tied to a certain numeric parameter of the analyzed system that influences length, width, radius, and other parameters. Activity that is coded in the figure zones is represented by the types of data representation axes, symmetry type, and coloring. The image can be divided into three strata horizontally: low, middle, and high strata. For example, Figure 2 shows four images from different social domains that can be used for making a decision about the management of a workforce, learning process, and research. The first image (a) is an image of a graduate student who studies courses through an online intelligent tutoring system (ITS), which should not just control the learning process but also explain its decisions using intuitive pictures. The second image (b) shows the data about an employee of a company (installer), whose direct supervisor uses an intelligent decision-supporting system to receive advice on motivating employees according to their performance. The third image (c) represents a funding application for a scientific project. The fourth image (d) is an image of a laboratory researcher according to the university's key performance indicators (KPI). These examples show individualized representation of particular systems in the UGVA notation. Let us consider the specifics of visualizing activity in the UGVA images, paying attention to the data axes and symmetry types for the example of the student who is shown in Figure 2a. The generalization of data, necessary for decision making, is based on the grouped components of the parametric model \(S_{p}\), where \(p\in[1,2,\ldots n,\ldots,m,\ldots u]\), measured at time \(t\). Then, each parameter \(n\) will be associated with an axis or its part in some zone of the anthropomorphic image (typically, zones of limbs are used). Usually, each group of parameters is mapped on a separate axis.