D.V. Berezkin1, V.Yu. Groshev2

1,2 Bauman Moscow State Technical University (Moscow, Russia)
 

Currently, graphic visualization is in demand in many areas of science and engineering technologies. In the era of "Big Data", the problem of data visualization becomes the most acute. More and more companies are faced with a large amount of data that is analyzed by specialists. In order to understand the success and quality of the analysis, to see the dynamics of indicators, it is important to provide a first-class visualization of the analysis obtained.

Analyze the capabilities of modern means of visualizing heterogeneous information. Clarify the possibility of supporting dynamic visualization and cross-platform systems. Provide an up-to-date choice of methods and tools for visualization subsystems of diversified tasks.

The analysis of modern means of visualizing heterogeneous information consisted in the analysis of existing techniques and functional compatibility, which are currently used for data visualization. The possibility of using augmented and virtual reality is analyzed, both advantages and possible disadvantages of this methodology are identified. An analysis was made among 10 common methodologies for displaying information in 3D.

Based on the hierarchical analysis, the Cesium API technology was chosen. Its standard functionality and shortcomings, the architecture of the Cesium platform and the scripting languages of this library are described.

Before the advent of modern analytics tools, data was placed in a spreadsheet and could be visually exported as a chart or graph. Now, in the era of big data, the job of reading and processing data falls to scientists, who apply new models and methods to extract useful information from large datasets. Big Data technologies also imply the visualization of the same processed data. In turn, we can conclude that the visualization that is obtained as a result of analysis by an expert may not always be clear to a person who is poorly versed in this area. However, it can be assumed that there are ways to visualize huge datasets that would immediately reveal important trends and dynamically display the corresponding changes.

The analysis of modern means of visualizing heterogeneous information for solving analytical problems consisted in the analysis of existing techniques and functional compatibility, which are currently used for data visualization. The possibility of using augmented and virtual reality is also analyzed, both advantages and possible disadvantages of this methodology are identified. At this stage, we can conclude that modern visualization tools using virtual and augmented reality open up new possibilities for data analysis. However, the use of these technologies may be limited by the specific characteristics of the visual system of the human eye. It is necessary to take into account all the main human characteristics in order to achieve a high-quality solution of analytical problems using visualization tools.

The next conclusion that can be drawn is the time it takes to analyze a large amount of data. Such an analysis usually takes longer due to the significant volume and highly fragmented data. The problem becomes much more complex when using AR devices. However, there is a solution to store data in the cloud. Cloud technologies are able to store a large amount of data and process resource-intensive tasks for a fixed time. Several applications [14] have proven the ability of cloud technologies to meet functional requirements in a timely manner.

Analysis and identification of the relevance of the choice of tools and frameworks for the development of a subsystem for 3D visualization of large data flows was carried out among 10 common methodologies for displaying information in 3D space. Based on the results of the analysis by the hierarchical method, the Cesium API technology was chosen. As a result, this technology can be integrated with the Cesium ion web platform, which provides reliable and secure storage, loading and visualization of additional content. Further, we can conclude that such a technology will provide a powerful ability to stream data from applications and workflows.

The following chapter described its standard functionality and shortcomings, touched on the Cesium architecture, and reviewed the library's scripting languages. There was also a considered and modern application of Cesium on a specific example. As a result of the application, this technology was distinguished by the flexibility of customizing the visualization of various data formats. In this case, the developed web application has the ability to visualize data over time and interactively interact with the 3D environment. All added objects can display relevant information when accessed. Due to this, we can conclude that this platform is the most relevant for working with large data flows in various professional fields.

Berezkin D.V, Groshev V.Yu. Analysis of modern tools for 3D visualization of big data for solving analytical problems. Dynamics of complex systems. 2022. V. 16. № 1. P. 22−34. DOI: 10.18127/j19997493-202201-03 (In Russian).

1. Friendly M. Milestones in the history of data visualization: a case study in statistical historiography. In: Classificationthe Ubiquitous Challenge. Springer. 2005. P. 34–52.
2. Stanley R, Oliveria M, Zaiane OR. Geometric data transformation for privacy preserving clustering. Departament of Computing Science. 2003.
3. Keim DA. Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Visual Comp Graph. 2000. P. 59–78.
4. Meijester A, Westenberg MA, Wilkinson MHF. Interactive shape preserving filtering and visualization of volumetric data. In: Proceedings of the Fourth IASTED International Conference. 2002. P. 640–643.
5. Saati T.L. Prinyatie reshenij pri zavisimostyah i obratnyh svyazyah: analiticheskie seti. M.: Librokom. 2009. 360 s.
6. Vorob'ev A.V., Vorob'eva G.R. Veb-orientirovannaya 2D/3D vizualizaciya parametrov geomagnitnogo polya i ego variacij. Nauchnaya vizualizaciya. 2017. T. 9. № 2. S. 94–101 (In Russian).
7. Starodubcev A.V., Slepenkov V.O. Frejmvork CesiumJS. Molodezhnyj nauchno – tekhnicheskij vestnik. 2016. № 12. S. 16–25.
8. Luz Caballero. An Introduction to WebGL – Part 1. Dev.Opera. 2011. [Elektronnyj resurs]. – Rezhim dostupa: URL: https://dev.opera.com/articles/introduction-to-webgl-part-1/ (data obrashcheniya: 03.03.2022) (In Russian).
9. Gruzdev S.S., ZHurkin I.G., Orlov P.YU., Pankin A.V. Analiz sostoyaniya i perspektiv po sozdaniyu elektronnogo kataloga ob"ektov okolozemnogo kosmicheskogo prostranstva. Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2016. T. 13. № 6. S. 51–59 (In Russian).
10. Patrick Cozzi. CZML in Cesium. CesiumGS – 2014. [Elektronnyj resurs]. – Rezhim dostupa: URL: https://github.com/CesiumGS/cesium/ wiki/CZML-in-Cesium (data obrashcheniya: 03.03.2022).
11. Sarah Chow. SIGGRAPH 2018 Autonomous Driving Simulation and Visualization BOF. Cesium 2018. [Elektronnyj resurs]. Rezhim dostupa: URL: https://cesium.com/blog/2018/07/31/autonomous-driving-bof-siggraph 2018. (data obrashcheniya 03.03.2022).
12. Sarah Chow. Propeller Platform: Software for Drone Surveys and Inspections. Cesium – 2017. [Elektronnyj resurs]. – Rezhim dostupa: URL: https://cesium.com/blog/2017.04.11.propeller-platform. (data obrashcheniya 03.03.2022).
13. Flightradar24 database. Advanced search Flightradar24/ [Elektronnyj resurs]. – Rezhim dostupa: URL: https://www.flightradar24.com/ data (data obrashcheniya 03.03.2022).
14. Zhanpeng Huang, Weikai Li, Pan Hui, Christoph Peylo. Cloudridar: A cloud-based architecture for mobile augmented reality. In Proceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems. ACM. 2014. P. 29–34