Support of the Decision-Making Process using the Unified Graphic Visualization of Activity (UGVA) Notation

Authors: Uglev V.A. Published: 28.09.2023
Published in issue: #3(144)/2023  
DOI: 10.18698/0236-3933-2023-3-125-140

Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing  
Keywords: decision making, cognitive visualization, Graph Mining, method "Chernoff Faces", afferent synthesis, UGVA method


The paper considers the problem of graphic support of the decision-making processes when working with a variety of complex multi-parameter objects that require comparison. It notes complexity of the visualization process, when it is necessary to visualize the object in dynamics with an emphasis on its activity and combine the categories of past, present and future (the afferent synthesis model according to Anokhin P.K.). Prerequisites and genesis of the Unified Graphic Visualization of Activity (UGVA) method appearance are described, as it the development of the well-known "Chernoff Faces" approach. Stages of implementing the methodology to form anthropomorphic images for visualization of the complex multi-parameter objects in the UGVA are described. Examples of the sets of images are provided, where curricula, student performance, project passports and employees’ activities in the workplace are considered as the objects for comparison. Methodological generalizations are proposed allowing a systematic approach to selection of option in visualizing the anthropomorphic images taking into account specifics of the information decomposition on the data axes and the types of symmetry. A coding system for various options of images in the UGVA is given that uses a combination of letters of the Latin and Greek alphabets

Please cite this article in English as:

Uglev V.A. Support of the decision-making process using the Unified Graphic Visualization of Activity (UGVA) notation. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2023, no. 3 (144), pp. 125--140 (in Russ.). DOI: https://doi.org/10.18698/0236-3933-2023-3-125-140


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