Cyber-social systems include humans who need to be modeled to study the system's behavior. Modeling human activity requires complex models which can be used to make an objective assessment of the person's professional activity and monitor the dynamics of his/her key performance indicators. The study proposes a methodology of complex formalization of data about a person in his/her educational, work, competence and other aspects based on the Unified Graphic Visualization of Activity (UGVA) method of cognitive visualization. This approach can be used to visualize the activity of one person and of the entire teams. We describe the proposed approach and give examples of its usage in industry (monitoring and motivation of employees) and education (individualization of training based on the automated intelligent tutoring system in higher education institution). The results obtained during the experiments are summarized: from the applied point of view, the peculiarities of teamwork in the formation of anthropomorphic images are given; from the technological point of view, a unified form of data storage adapted for the UGVA method is applied; psychological features of image perception in the proposed notation are noted. Then recommendations on complex application of the proposed methodology and limitations of its application are made. The novelty of the presented results consists in summarizing the experience of a number of projects of cyber-social systems oriented on complex visualization of human activity results, based on the UGVA graphical notation.
AlekhinA.AlekseevO. (2022). Organizational balance as a key to enterprise reorganization. Vestnik of the Plekhanov Russian University of Economics, 2022(19), 190–205. (in Russian). https://doi.org/10.21686/2413-28292022-5-190-205
2.
BlömekeS.KaiserG.KönigJ.JentschA. (2020). Profiles of mathematics teachers’ competence and their relation to instructional quality. ZDM, 2020(52), 329–342. https://doi.org/10.1007/s11858-020-01128-y
3.
CeneE.ParimC.ÖzkanB. (2018). Comparing the performance of basketball players with decision trees and TOPSIS. Data Science Applications, 2018(1), 21–28.
4.
ChernoffH. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 1973(342), 361–368. https://doi.org/10.1080/01621459.1973.10482434
5.
FewS. (2006). Information dashboard design: The effective visual communication of data. O’Reilly Media, Inc.
FluryB.RiedwylH. (1981). Graphical representation of multivariate data by means of asymmetrical faces. Journal of the American Statistical Association, 1981(76), 757–765. https://doi.org/10.1080/01621459.1981.10477718
8.
GavrilovaT.KhoroshevskyV. (2000). Knowledge bases of intellectual systems. Piter. (in Russian).
9.
GavrilovaT.KuznetsovaA.LeshevaI. (2023). On the issue of the development of ontologies of scientific and academic work. XXI national Conference of Artitificial Intelligence, 2023(1), 60–67. (in Russian).
10.
GorbanA.ZinovyevA. (2015). Fast and user-friendly non-linear principal manifold learning by method of elastic maps. In IEEE International conference on data science and advanced analytics (DSAA), 2015 (pp. 1–9). https://doi.org/10.1109/DSAA.2015.7344818
11.
HanJ.KamberM.PeiJ. (2011). Data mining concepts and techniques third edition. Morgan Kaufmann Ser. Data Manag. Syst., 2011(5), 83–124.
12.
HorváthI. (2022). Designing next-generation cyber-physical systems: Why is it an issue?Journal of Integrated Design and Process Science, 2022(26), 317–349. https://doi.org/10.3233/JID-220008
13.
JacksonP. (1986). Introduction to expert systems. Addison-Wesley Pub. Co.
14.
JonesD. (1982). The inventions of Daedalus: A compendium of plausible schemes. WH Freeman.
15.
KendalS.CreenM. (2007). An introduction to knowledge engineering. Springer.
16.
KukharevG.KaziyevaN. (2020). Digital facial anthropometry: Application and implementation. Pattern Recognit. Image Anal., 2020(30), 496–511. https://doi.org/10.1134/S1054661820030141
17.
Le Corbusier (1948). Le Modulor. Boulogne-sur-Seine: Editions de l’Architecture d’Aujoud’hui: Paris, France. (in France).
18.
MillerG. (1956). The magical number seven, plus or minus two: Some limits on ourcapacity for processing information. Psychological Review, 1956(63), 81–97. https://doi.org/10.1037/h0043158
19.
MurtinhoV. (2015). Leonardo’s vitruvian man drawing: A new interpretation looking at leonardo’s geometric constructions. Nexus Network Journal, 17, 507–524. https://doi.org/10.1007/s00004-015-0247-7
20.
NuñesJ. (2010). Ideas para el uso de las caras de Chernoff en la cartografia escolar. Boletim de Geografia, 28(1), 5–15.
21.
OsipovG. (2002). From situational control to applied semiotics. News of Artificial Intelligence, 2002(6), 3–7. (in Russian).
22.
ParetoL.SnisU. L. (2007). An interactive visualization model for competence management: An integrative approach. Proc. I-KNOW, 2007(7), 440–447.
23.
PeerM.BrunecI.NewcombeN.EpsteinR. (2021). Structuring knowledge with cognitive maps and cognitive graphs. Trends in Cognitive Sciences, 2021(25), 37–54. https://doi.org/10.1016/j.tics.2020.10.004
24.
PospelovD.OsipovG. (1999). Applied semiotics. News of Artificial Intelligence, 1999(1), 9–35. (in Russian).
25.
QinX.LuoY.TangN.LiG. (2020). Making data visualization more efficient and effective: A survey. The VLDB Journal, 2020(29), 93–117. https://doi.org/10.1007/s00778-019-00588-3
26.
RezaeiF.KhalilzadehM.SoleimaniP. (2021). Factors affecting knowledge management and its effect on organizational performance: Mediating the role of human capital. Advances in Human-Computer Interaction, 2021, 1–16. https://doi.org/10.1155/2021/8857572
27.
RodriguezJ.KaczmarekP. (2016). Visualizing financial data. John Wiley & Sons.
28.
RosenfeldH.MooreS.NostE.RothR.VincentK. (2017). Chernoff zombies. North America Cartographic Information Society, 2017(12).
29.
SirtsevaV.ZabavinaA.ZalybovinV.KazaryanK.SaykinaM.TelenkovR.ChernovA.BayramkulovaL. (2023). The white book of the digital economy 2023. ANO Digital Economy. (in Russian).
UedaK.TakahashiT.NodaT.YanagisawaH.MurakamiT. (2016). Cognitive and neural mechanisms of aesthetic sensitivity with regard to product form. Journal of Integrated Design and Process Science, 2016(20), 61–72. https://doi.org/10.3233/jid-2016-0016
32.
UglevV. (2022). Unified graphic visualization of activity (UGVA) method. Novel and Intelligent Digital Systems. LNNS, 2022(556), 255–265. https://doi.org/10.1007/978-3-031-17601-2_25
33.
UglevV. (2023a). 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(144), 125–140(in Russian). https://doi.org/10.18698/0236-3933-2023-3-125-140
34.
UglevV. (2023b). Visual decision support for curriculum development using the UGVA method. Scientific and Technical Information Processing, 50(6), 646–654. https://doi.org/10.3103/S0147688223060163
35.
UglevV. (2024). Implementation of decision-making mechanism in the Intelligent Tutoring System based on the Expert Systems module. Pattern Recognit. Image Anal., 2024(34), 746–752. https://doi.org/10.1134/S1054661824700615
36.
UglevV.GavrilovaT. (2022). Cross-cutting visual support of decision making for forming personalized learning spaces. Novel and Intelligent Digital Systems. LNNS, 2022(556), 3–12. https://doi.org/978-3-031-17601-2_1
37.
UglevV.KuznetsovM.MeshkovS. (2024). Approach to the formation and visualization of the competency profile of the staff of organizations using the UGVA method. Computational Science and Its Applications(ICCSA 2024). LNCS, 2024(14814), 170–185. https://doi.org/10.1007/978-3-031-64608-9_11
38.
UglevV.ShanginaE. (2023). Assessment and visualization of course-level and curriculum-level competency profiles. Computational Science and Its Applications(ICCSA 2023). LNCS, 2023(14104), 478–493. https://doi.org/10.1007/978-3-031-37105-9_32
39.
UglevV.SmirnovG. (2024). Combined maps as a tool of concentration and visualization of knowledge in the logic of operation of the intelligent tutoring systems. Intelligent Tutoring Systems. LNCS, 2024(14798), 27–39. https://doi.org/10.1007/978-3-031-63028-6_3
40.
UglevV.SychevO. (2022). Evaluation, comparison and monitoring of multiparametric systems by unified graphic visualization of activity method on the example of learning process. Algorithms, 2022(152), 468. https://doi.org/10.3390/a15120468
41.
WanT.WangB. (2021). An integrated social and behavioral system approach to evaluation of healthcare information technology for polychronic conditions. Journal of Integrated Design and Process Science, 2021(25), 148–160. https://doi.org/10.3233/JID-200011
42.
WerrmannJ.KrämerB. (2017). Knowledge management through ontology-driven integration of disparate knowledge sources. Journal of Integrated Design and Process Science, 2017(21), 25–39. https://doi.org/10.3233/jid-2017-0017
43.
WeylH. (2015). Symmetry. Princeton University Press. 2015(47).
44.
WeynsD.AnderssonJ.CaporuscioM.FlamminiF.KerrenA.LöweW. (2021). A research agenda for smarter cyber-physical systems. Journal of Integrated Design and Process Science, 2021(25), 27–47. https://doi.org/10.3233/JID210010
45.
ZadehL.AlievR. (2018). Fuzzy logic theory and applications: part I and part II. World Scientific Publishing.
