Abstract
The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected with sensors installed on them. Multilevel knowledge graphs (KG) are used to describe the observed objects. The new adaptive synthesis method develops previously proposed inductive and deductive synthesis methods, allowing the context to be taken into account when predicting the states of the monitored objects based on the data obtained from them. The article proposes the algorithm for the suggested method and presents its computational complexity analysis. The software system, based on the proposed method, and the algorithm for multilevel adaptive synthesis of the object models developed, are described in the article. The effectiveness of the proposed method is shown in the results from modeling the states of telecommunication networks of cable television operators.
Highlights
We propose a solution to the problem of adaptive deductive synthesis of monitoring objects, by defining the context at each step of the synthesis, based on the object parameter values coming from the monitoring systems, and applying policies that allow for a given context to determine the transition rules used in the synthesis
The proposed method of multilevel adaptive synthesis of monitoring objects allows solving the class of problems, of predicting the state of complex objects with variable structures, taking into account the context in which the objects are operating
The context is understood as the external conditions of the monitoring object functioning, which influence the state of the object
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Attributes of nodes and edges are the parameters of the complex object, the values of which characterize the state of the corresponding element of the monitoring object at a specified moment. When predicting the state of the monitored object, the task is to determine the values of attributes of nodes and edges of graph Gtk at time point tk+T , i.e., to construct graph Gtk+T. We propose a solution to the problem of adaptive deductive synthesis of monitoring objects, by defining the context at each step of the synthesis, based on the object parameter values coming from the monitoring systems, and applying policies that allow for a given context to determine the transition rules used in the synthesis
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