Abstract
In article discusses the approach to development of mathematical software for support the process of managing the data schema in relational DBMS in terms of processing of parallel queries stream that compete for data in the hierarchy of the DBMS core memory. The necessity of the formation of a parametric model of queries competitive access. Briefly discusses methods of machine learning, allowing to solve the problem of regression recovery. The use of the random forest method as the most universal method of approximation of arbitrary functions is substantiated. A method of forming a parametric model of competitive access based on the random forest method, as well as an approach with the ensemble of sets of decision trees, which allows to provide the required generalizing ability and stability of the model to partial features and diversity of all types of queries received at the input of the DBMS. The stages of the developed algorithms are presented: ranking query parameters by total execution time and automatic data distribution, allowing you to go from approximating the target system with linear-continuous functions to a set of logical data schema objects, ordered by their effect on time, total query execution time, reducing multi-criteria optimization task to a task optimization by one criterion.
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