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ML Support for Conformity Checks in CMDB-Like Databases

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14126))

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Abstract

During data input into databases like CMDB (Configuration Management Databases) we usually have a conformity check. The conventional approach is to create hundreds of rules to check data quality. This article shows several ideas on how to use ML algorithms to support the quality management of CMDB. We focus on naming conventions commonly used in CI (Configuration Items) - attributes like hostnames, serial numbers, and application names. Such attributes should be consistent with some dictionary data (operating system names, vendors) and existing relationships (location, applications). We review several strategies for feature extraction including tokenization and analyze the usability of CNB, RVAE, or NN to this particular problem. We also show the results of experiments on a public dataset (USA car database) to demonstrate the efficiency and inspire other researchers to work on similar topics. Algorithms used in the experiment are published as Jupiter Lab files.

This work is supported by the Polish Minister of Education and Science as part of an implementation doctorate, grant No. DWD/5/0286/2021.

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Authors and Affiliations

  1. Wroclaw University of Science and Technology, Wrocław, Poland

    Szymon Niewiadomski & Grzegorz Mzyk

Authors
  1. Szymon Niewiadomski
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  2. Grzegorz Mzyk
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Correspondence to Szymon Niewiadomski .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Niewiadomski, S., Mzyk, G. (2023). ML Support for Conformity Checks in CMDB-Like Databases. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_33

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  • DOI: https://doi.org/10.1007/978-3-031-42508-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42507-3

  • Online ISBN: 978-3-031-42508-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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