Keywords: knowledge graph, autoML, telecommunication network, meta-learning, meta-mining
Analysis of telecommunication networks state using knowledge graphs and controlled automatic machine learning
Nowadays, knowledge graphs are used as a model of telecommunication networks and for storing data on their state. Knowledge graphs make it possible to combine within one model many particular models of information systems used by operators, which allow joint analysis of data from various sources and, as a result, increase the efficiency of solving network management tasks. Knowledge graph helps to solve complex problems. Filling the knowledge graph requires processing large amounts of raw data. For their processing, it is necessary to use machine learning algorithms, which is difficult when building such models due to the fact that the configurations of modern networks change over time, which requires frequent reconfiguration of machine learning algorithms. In addition, automated machine learning algorithms have a high computational complexity. The purpose of the research is to develop an approach that makes it possible to employ automated machine learning (AutoML) to analyze live data coming from the network by means of metamining capabilities to control the choice of machine learning algorithms and the selection of hyperparameters. The method of determining the state of a telecommunications network using both managed machine learning and metamining, followed by building a network model in the form of a knowledge graph, was utilized. An approach has been developed to provide controlled machine learning when building models of telecommunication networks in the form of a knowledge graph, which has a reduced computational complexity by decreasing the number of candidate algorithms supplied to the AutoML input. The statement and solution of the problem of classifying the state of the vehicle according to the data coming from the network are given; a description of the monitoring system based on the use of the proposed approach is presented. The application of the approach is illustrated by the example of solving the task of determining the state of cable TV operator's network.
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Keywords: knowledge graph, autoML, telecommunication network, meta-learning, meta-mining
For citation: Kulikov I.A. Zhukova N.A. Tianxing M. Analysis of telecommunication networks state using knowledge graphs and controlled automatic machine learning. Modeling, Optimization and Information Technology. 2023;11(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1319 DOI: 10.26102/2310-6018/2023.41.2.005 (In Russ).