Visualization in Data Reconstruction Tasks

Abstract

Many application tasks of multidimensional data analysis which describe the state of real physical or other systems face with difficulties. This is a consequence of the low-quality source data, including missing values, the probability of errors or unreliability of measurements. Incomplete data can become an obstacle for research using many modern informational methods. The current work examines the potential and capabilities of visual analytics tools for preliminary preparation, correction or complete analysis of primary data volumes. A promising area of application of the approach discussed in the study is the targeted use of visualization capabilities as a data analysis tool. The implementation of specialized visual metaphors is used to solve problems of processing and interpreting data, the sources of which are cyberphysical systems of different complexity levels. Such systems operate in an autonomous or partially controlled mode. A characteristic feature of these systems is the presence of a large number of sensors that collect various types of data. Such data differ in the capacity of the corresponding information channels, their speed and reliability. Examples of such cyberphysical systems are unmanned aerial vehicles (UAVs), robotic stations, and multimodal monitoring systems. These systems can function in conditions where it is difficult to obtain objective observation experience (deep-sea robots). The effective use of data collected by cyberphysical monitoring systems is a condition for solving a large number of application and research tasks.