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Forecasting Telecommunication Network States on the Basis of Log Patterns Analysis and Knowledge Graphs Modeling

Forecasting Telecommunication Network States on the Basis of Log Patterns Analysis and Knowledge Graphs Modeling

Kirill Krinkin, Alexander Ivanovich Vodyaho, Igor Kulikov, Nataly Zhukova
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 27
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781683181774|DOI: 10.4018/IJERTCS.311464
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MLA

Krinkin, Kirill, et al. "Forecasting Telecommunication Network States on the Basis of Log Patterns Analysis and Knowledge Graphs Modeling." IJERTCS vol.13, no.1 2022: pp.1-27. http://doi.org/10.4018/IJERTCS.311464

APA

Krinkin, K., Vodyaho, A. I., Kulikov, I., & Zhukova, N. (2022). Forecasting Telecommunication Network States on the Basis of Log Patterns Analysis and Knowledge Graphs Modeling. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 13(1), 1-27. http://doi.org/10.4018/IJERTCS.311464

Chicago

Krinkin, Kirill, et al. "Forecasting Telecommunication Network States on the Basis of Log Patterns Analysis and Knowledge Graphs Modeling," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 13, no.1: 1-27. http://doi.org/10.4018/IJERTCS.311464

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Abstract

The article proposes a state forecasting method for telecommunications networks (TN) that is based on the analysis of behavioral models observed on users' network devices. The method applies user behavior that makes it possible to forecast with more accuracy both the network parameters and the load at various back-ends. Suggested forecasts facilitate implementing reasonable reconfiguration of the TN. The new method proposed as a further development of TN states the forecasting method presented by the authors before. In this new version, forecasting algorithm users' behavioral models are involved. The models refer to a class of time diagrams of device transitions between different states. The novelty of the proposed method is that resulting TN models enable forecasting device state transitions represented in a device state diagram in the form of knowledge graph, in particular changes in loads of different back-ends. The provided case study for a subgroup of network devices demonstrated how their states can be forecasted using behavioral models obtained from log files.

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