This article investigates the problem of structured data schema matching and aggregates the results from previous stages of the research. The systematization of results demonstrated that while the previously considered approaches show promising outcomes, their effectiveness is often insufficient for real-world application. One of the most effective methods was selected for further study. The Self-Organizing Map method was analyzed, which is based on a criterial analysis of the attribute composition of schemas, using an iterative approach to minimize the distance between points (in the current task, a point represents a schema attribute). An experiment on schema matching was conducted using five examples. The results revealed both the strengths and limitations of the method under investigation. It was found that the selected method exhibits insufficient robustness and reproducibility of results on diverse real-world datasets. The verification of the method confirmed the need for its further optimization. The conclusion outlines directions for future research in this field.

Keywords: data management, fusion schemes, machine learning, classification, clustering, machine learning, experimental analysis, data metrics

The mathematical model for automation of search and an assessment of anomalies of volume of a network traffic is offered. The developed model can be used in a traffic control system for the analysis of a condition of the computer network for the purpose of detection of malfunctions of the network equipment, identification of casual and deliberate actions from users, and also action of malefactors.
The offered scheme can be used in a control system of a traffic both internal, and an external network. Input parameters can be described by qualitative values that allow developing base of rules for elaboration of response to the arisen situation.

Keywords: Computer Networks, analysis and forecasting network traffic, anomalies in traffic.