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An Analytical Computing Infrastructure for Monitoring Dynamic Networks Based on Knowledge Graphs

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Book cover Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

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Abstract

Dynamic network monitoring systems are typically designed to solve a predefined number of tasks, new requirements lead to expensive development efforts and sometimes even require changes in the system architecture. Knowledge graphs are powerful and flexible tools for information integration and supported by a set of standardized vocabularies and languages (the “Sematic Web” toolset). In this work, we discuss the application of knowledge graphs to develop and analyze an analytical computing infrastructure for a dynamic network monitoring system. As a typical dynamic network, a multiservice telecommunication network is considered. The presented system combines static models of a telecommunication network and dynamic monitoring data and makes it possible to obtain complex analytical reports using SPARQL queries over the knowledge graph. Those reports are of crucial importance to network stakeholders for improving the network services and performance. First, we analyze problems solved by traditional monitoring systems, and identify the classes of problems such systems cannot solve. Then we propose an analytical monitoring system architecture based on knowledge graphs to address these classes of problems. We present the system structure and detailed descriptions of the ontological and mathematical models of the resulting knowledge graph. In order to test the architecture discussed, we create an example task of the analytical monitoring system and analyze system performance depending on the size of the knowledge graph. The results of the analysis are presented using a number of SPARQL queries.

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Acknowledgment

To Metaphacts GmbH, Daimlerstrasse 36, 69190, Walldorf, Germany for the license to model knowledge graphs on the Metaphactory platform.

Funding

The research was funded by Russian Foundation for Basic Research (RFBR) according to the research projects #18-57-34001 and #19-07-00784.

Author information

Authors and Affiliations

  1. Saint-Petersburg Electrotechnical University “LETI”, Saint-Petersburg, Russia

    Igor Kulikov & Yulia Shichkina

  2. ITMO University, St. Petersburg, Russia

    Gerhard Wohlgenannt

  3. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Nataly Zhukova

Authors
  1. Igor Kulikov
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  2. Gerhard Wohlgenannt
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  3. Yulia Shichkina
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  4. Nataly Zhukova
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Corresponding author

Correspondence to Yulia Shichkina .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Kulikov, I., Wohlgenannt, G., Shichkina, Y., Zhukova, N. (2020). An Analytical Computing Infrastructure for Monitoring Dynamic Networks Based on Knowledge Graphs. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

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