PROCESSING DATA AND GENERATING RECOMMENDATIONS IN MARKETING TESTING

Abstract

This scientific article explores a structural model of computational processes that are crucial for the effective processing of data and the generation of accurate recommendations in the context of marketing testing. Attention is paid to the application of these processes in unstable cloud environments, which adds significant complexity to management and optimization. The proposed model describes a system as a set of interconnected tasks that are performed on a regular periodic basis, ensuring a continuous cycle of analysis and forecasting. A central aspect of the research is the development of innovative methods for managing these computational processes. In particular, the article focuses on the critically important problem of minimizing delays concerning established deadlines for task execution. Furthermore, significant attention is given to optimizing the use of available computational resources, which allows for increasing system efficiency and reducing costs. The article also analyzes in detail important characteristics of tasks, such as their idempotency, classification by execution priority, and by the volume of computational resources consumed. Finally, the article proposes a simulation of the system's functioning under conditions of cloud environment instability