Information System for Visualizing Relationships between Quality Factors of a Technological Process

Abstract

The study indicates that the quality of a technological process is influenced by a specific set of factors. Each of these factors establishes a relationship in the form of either a direct or an indirect connection. As an illustrative example, the process of raster image creation was examined. The set includes the most significant factors, namely: resolution, color depth, color model, file format, file size, image dimensions, compression, brightness, saturation, and sharpness. The formation of interconnections among these factors was based on previously obtained expert assessments. A semantic network was developed to represent the directions and types of influence among the identified factors.

To formalize the revealed relationships, a reachability matrix was constructed. It is represented as a two-dimensional array, in which each row and each column corresponds to a particular factor. The dependencies between factors are identified by the presence of ones in the relevant positions. A decomposition of the factor set was performed based on the matrix structure. Separate graphical models were developed for both direct and indirect influences. Each node of the hierarchical tree represents a relation between specific quality parameters. The resulting structures allow the identification of priority factors that exert the greatest influence on the technological process under investigation.

Based on the proposed method for analyzing the relationships among quality factors of a technological process, an information system was developed for their visualization. The core of the system consists of a set of modules, each of which performs a specific functional task. These include modules for configuration, input data processing, interface management, matrix construction, analytical computations, diagram generation, graph visualization, and coordination of interface components. The software implementation supports interactive input of the number of factors, proper matrix formation, content scaling, and adaptive result presentation. Visualization of factor influences enables structural evaluation of interconnections and identification of key patterns in the formation of quality indicators. The software architecture is designed to support extension, modification, and reuse in similar analytical tasks.