###### Abstract  
The article discusses the problem of visualization of complex multiparameter systems, defined by datasets on their structure, functional structure, and activity in the form of complex graphs and transition of traditional representation of the data acquired by graph mining to a compact image built by pictographic methods. In these situations, we propose using the Unified Graphic Visualization of Activity (UGVA) method for data concentration and structuring. The UGVA method allows coding in an anthropomorphic image of elements of graphs with data on structural and functional features of systems and overlaying these images with the data on the system's activity using coloring and artifacts. The image can be composed in different ways: it can include the zone of integral evaluation parameters, segmented data axes of five types, and four types of symmetry. We describe the method of creating UGVA images, which consists of 13 stages: the parametric model is represented as a structural image that is converted to a basic image that is then detailed into the particular image by defining geometric parameters of the primitives and to the individualized image with the data about a particular object. We show how the individualized image can be overlaid with the operative data as color coding and artifacts and describe the principles of interpreting UGVA images. This allows solving tasks of evaluation, comparison, and monitoring of complex multiparameter systems by showing the decision-maker an anthropomorphic image instead of the graph. We describe a case study of using the UGVA method for visualization of data about an educational process: curricula and graduate students, including the data mined from the university's learning management system at the Siberian Federal University for students majoring in \"informatics and computing\". The case study demonstrates all stages of image synthesis and examples of their interpretation for situation assessment, monitoring, and comparison of students and curricula. It allowed for finding problematic moments in learning for individual students and their entire group by analyzing the development of their competence profiles and formulating recommendations for further learning. The effectiveness of the resulting images is compared to the other approaches: elastic maps and Chernoff faces. We discuss using graph mining to generate learning problems in order to lessen the workload of gathering raw data for the UGVA method and provide general recommendations for using the UGVA method based on our experience of supporting decision making. visual data mining; graph mining; decision making support; cognitive graphics; pictographs; Chernoff faces; UGVA method; e-learning +
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generate a compact description of the analyzed system but also make it easily interpreted for the decision maker. Many researchers showed that cognitive visualization significantly increases the efficiency of interpreting complex data [1; 2; 3; 4]. The modern practice of data mining [5], including graph mining, uses a number of graphical notations that allow for making decisions more quickly. The technique of visualizing complex systems can be based on structural matching of the relevant entities, forming structures and hierarchies of different objects--or it can be based on their semantic linking, forming concept networks, e.g., ontologies [6]. During graph mining, one big graph can be divided into subgraphs, which allows for formulating tasks such as finding typical or non-typical subgraphs for further decision making. This allows for using the regular way of describing graph vertices and edges (e.g., adjacency matrix). However, these methods do not work for describing a complex, multiparameter system or for the decision-making task for analyzing an array of such systems. This requires generalizing the raw source data that should be visualized for the decision maker.