###### Abstract
Regular methods of dimensionality reduction of a parametric space (e.g., converting to Euclidean space and principal component analysis visualization) hide the information of the system's structure, which makes interpreting visualization difficult. In practical decision-making situations requiring visualizing dynamics of a group of multiparameter systems (e.g., employees, students, projects, tender applications, a company, etc. ), they are often visualized one by one, detailing the structure of each system. This further complicates the analysis and decision making. We think that an effective approach for these tasks requires a visualization method that allows for concentrating information about complex systems described by heterogeneous datasets into a compact image but also allows for monitoring dynamics of key variables in the analyzed pool of objects. In this article, we propose such an approach and study a case of its usage. The method should be used to analyze different kinds of systems: technical, social, biological, etc. The more complex the analyzed system is, the more difficult it is to represent it as a compact image while preserving the structural information about the system. A common example of this kind of system is a person in the context of a particular activity (business, learning, etc. ): for a good description, we need a parametric model of the activity and its participants and a method of comparison with other people in the target group to simplify monitoring the dynamics, selection, and management of people during their activities. Another example of a complex system for visualization is university curricula and the actions of the collective of students according to their curriculum in order to master their field of study. The emphasis will be on the cognitive visualization of the set of analyzed systems (from curricula to learning situations of students) and generating recommendation for decision maker both in the context of the case study and regarding the general usage of the proposed method. The goal of this article is describing the UGVA method for visual comparison of complex multiparameter systems while taking into account dynamics of their parameters (activity or behavior). Describing complex objects and systems is based on their presentation as a set of entities (subsystems, elements, functions, metrics) that are linked by certain relationships. Such objects can be represented as a graph \(G(Q,L)\), where \(Q\) and \(L\) are non-empty sets of vertices and edges, respectively. Each vertex and edge can be described by their own set of parameters; the edges can be homogeneous or heterogeneous. Consider graph \(G\), which is a composition of several sets of structures:  
* \(G_{w}\)--the graph of structural decomposition of the analyzed system (normally, structural models of system form the hierarchy of its parts and use edges like aggregation or composition);
* \(G_{z}\)--graph of the functional component of the analyzed system (normally, as a tree of functions or goals);
* \(G_{r}\)--graph of the development of a particular process in the system or changing the state (trajectory) of an actor along the \"bottom layer\" of the elements of graphsand \(G_{2}\) (representation of individual actions), including temporal characteristics of this process (normally, it is a trajectory or footprint; see Figure 1). We define \(G\) as a tuple (1):  
\[G(Q,L)=<G_{w}\cup G_{z}\cup G_{r}|\Omega>, \tag{1}\]  
where \(\Omega\) is the adjacency matrix for the different parameters in \(G\). This representation is interesting because, having the data from the \(G_{r}\) layer, we can interpret the current situation for the analyzed system (from the semiotic triangle syntax-semantics-pragmatics [7]) and assess its dynamics. The components of \(G\) and values in \(\Omega\), in practice, can be part of different datasets. We need a visual representation that allows for both demonstrating all the components of \(G\) and the dynamics of this graph structure.