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Conceptual Model Engineering for Industrial Safety Inspection Based on Spreadsheet Data Analysis

Part of the Communications in Computer and Information Science book series (CCIS,volume 1126)

Abstract

Conceptual models are the foundation for many modern intelligent systems, as well as a theoretical basis for conducting more in-depth scientific research. Various information sources (e.g., databases, spreadsheets data, and text documents, etc.) and the reverse engineering procedure can be used for creation of such models. In this paper, we propose an approach to support the conceptual model engineering based on the analysis and transformation of tabular data from CSV files. Industrial safety inspection (ISI) reports are used as examples for spreadsheets data analysis and transformation. The automated conceptual model engineering involves five steps and employs the following software: TabbyXL for extraction of canonical (relational) tables from arbitrary spreadsheet data in the CSV format; Personal Knowledge Base Designer (PKBD) for generation of conceptual model fragments based on analysis and transformation of canonical tables, and aggregating these fragments into domain model. Verification of the approach was carried out on the corpus containing 216 spreadsheets extracted from six ISI reports. The obtained conceptual models can be used in the design of knowledge bases.

Keywords

  • Spreadsheet data
  • Conceptual models
  • Class diagram
  • UML
  • Model transformation
  • Industrial safety inspection

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Acknowledgments

This work was supported by the Russian Science Foundation, grant number 18-71-10001.

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Correspondence to Aleksandr Yu. Yurin .

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Dorodnykh, N.O., Yurin, A.Y., Shigarov, A.O. (2020). Conceptual Model Engineering for Industrial Safety Inspection Based on Spreadsheet Data Analysis. In: Simian, D., Stoica, L. (eds) Modelling and Development of Intelligent Systems. MDIS 2019. Communications in Computer and Information Science, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39237-6_4

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  • DOI: https://doi.org/10.1007/978-3-030-39237-6_4

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