Subjective Interpretation in Visualization

Author:

Zakharova A.A.1ORCID,Shklyar A.V.2ORCID,Vekhter E.V.2ORCID

Affiliation:

1. V.A. Trapeznikov Institute of Control Sciences RAS

2. Tomsk Polytechnic University

Abstract

The paper considers the problem of the influence of subjective factors on the effectiveness of the use of visualization tools in the tasks of interpretation and practical analysis of unformalized data. Subjectivity is the starting point in the cognitive search, analysis of new information and proposing new hypotheses. In the methods of studying unformalized data, including the use of modern intelligent methods, the emergence of the stage of interactive communication between the researcher and the data, allowing to combine the data and their possible subjective interpretation in one information space, can give new tools for scientific research and methods of their application Based on the developed semiotic model of visualization, the system of initiating factors that have a significant impact on the performance of the tools is proposed It is shown that visualization tools, their perceived characteristics and control subsystems can form the necessary emotional factors in the user to control his activity. A controllability hypothesis has been formulated to explain the limitations of some visualization systems and to avoid errors in the design of new visual analytics tools. Thus, the design of visualization tools is considered as a complex parameter for managing their purpose and effectiveness. Examples are given to illustrate the validity of the assumptions made and their practical relevance.

Publisher

Keldysh Institute of Applied Mathematics

Reference10 articles.

1. C. Vieira, P. Parsons, V. Byrd, «Visual learning analytics of educational data: A systematic literature review and research agenda», Computers and Education, 2018, doi: 10.1016/j.compedu.2018.03.018.

2. D. Sacha, «Human-centered machine learning through interactive visualization», 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, April, pp. 27–29, 2016.

3. G. K. L. Tam, V. Kothari, M. Chen, «An Analysis of Machine- and Human-Analytics in Classification», IEEE Transactions on Visualization and Computer Graphics, v. 23, pp. 71–80, 2017, doi: 10.1109/TVCG.2016.2598829.

4. A. Zakharova, A. Shklyar, E. Vekhter, «Semiotic Assessment of Visualization Tools», в Proceedings of the 31st International Conference on Computer Graphics and Vision (GraphiCon 2021), Nizhny Novgorod, Russia, сен. 2021, т. 3027, сс. 288–295. Просмотрено: 7 июль 2022 г. [Онлайн]. Доступно на: http://ceur-ws.org/Vol-3027/#paper28.

5. A. Zakharova, E. Vekhter, A. Shklyar, «Adaptable Visualization», Scientific Visualization, v. 13, pp. 67–78, 2021, doi: 10.26583/sv.13.2.05.

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