Improving stream data processing efficiency in an intelligent educational system

In the context of the development of intelligent training and educational systems, there is a need to process streaming data while meeting strict quality of service requirements, such as memory consumption, prediction accuracy, response time in the face of dynamic data changes, and others. The challenge lies in the fact that traditional data processing methods often fail to provide the required quality under limited resources. Aim of article is to develop a mathematical model for streaming data processing in an intelligent educational system and conduct an experimental evaluation of the effectiveness of the software components developed based on the model within the specified quality of service constraints. A mathematical model of data streaming processing in an intelligent educational system based on lambda calculus and corresponding formalizations is described. Based on the mathematical model, a modification of the incremental algorithm is developed.