Skip to main content
SpringerLink
Log in
Book cover

International Conference on Modelling and Development of Intelligent Systems

MDIS 2019: Modelling and Development of Intelligent Systems pp 51–65Cite as

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

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Berman, A.F., Nikolaichuk, O.A., Yurin, A.Y., Kuznetsov, K.A.: Support of decision-making based on a production approach in the performance of an industrial safety review. Chem. Petrol. Eng. 50(11–12), 730–738 (2015). https://doi.org/10.1007/s10556-015-9970-x

    Article  Google Scholar 

  2. Yurin, A.Y., Dorodnykh, N.O., Nikolaychuk, O.A., Grishenko, M.A.: Prototyping rule-based expert systems with the aid of model transformations. J. Comput. Sci. 14(5), 680–698 (2018). https://doi.org/10.3844/jcssp.2018.680.698

    Article  Google Scholar 

  3. TabbyXL wiki. https://github.com/tabbydoc/tabbyxl/wiki/Industrial-Safety-Inspection. Accessed 13 Sept 2019

  4. Shigarov, A.O., Mikhailov, A.A.: Rule-based spreadsheet data transformation from arbitrary to relational tables. Inf. Syst. 71, 123–136 (2017). https://doi.org/10.1016/j.is.2017.08.004

    Article  Google Scholar 

  5. Mauro, N., Esposito, F., Ferilli, S.: Finding critical cells in web tables with SRL: trying to uncover the devil’s tease. In: 12th International Conference on Document Analysis and Recognition, pp. 882–886 (2013). https://doi.org/10.1109/ICDAR.2013.180

  6. Adelfio, M., Samet, H.: Schema extraction for tabular data on the web. VLDB Endowment 6(6), 421–432 (2013). https://doi.org/10.14778/2536336.2536343

    Article  Google Scholar 

  7. Chen, Z., Cafarella, M.: Integrating spreadsheet data via accurate and low-effort extraction. In: 20th ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp. 1126–1135 (2014). https://doi.org/10.1145/2623330.2623617

  8. Embley, D.W., Krishnamoorthy, M.S., Nagy, G., Seth, S.: Converting heterogeneous statistical tables on the web to searchable databases. IJDAR 19(2), 119–138 (2016). https://doi.org/10.1007/s10032-016-0259-1

    Article  Google Scholar 

  9. Rastan, R., Paik, H., Shepherd, J., Haller, A.: Automated table understanding using stub patterns. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 533–548. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_33

    Chapter  Google Scholar 

  10. Goto, K., Ohta, Yu., Inakoshi, H., Yugami, N.: Extraction algorithms for hierarchical header structures from spreadsheets. In: Workshops of the EDBT/ICDT 2016 Joint Conference, vol. 1558, pp. 1–6 (2016)

    Google Scholar 

  11. Nagy, G., Seth, S.: Table headers: An entrance to the data mine. In: 23rd International Conference Pattern Recognition, pp. 4065–4070 (2016). https://doi.org/10.1109/ICPR.2016.7900270

  12. Koci, E., Thiele, M., Romero, O., Lehner, W.: A machine learning approach for layout inference in spreadsheets. In: Proceedings of 8th International Joint Conference Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 77–88 (2016). https://doi.org/10.5220/0006052200770088

  13. de Vos, M., Wielemaker, J., Rijgersberg, H., Schreiber, G., Wielinga, B., Top, J.: Combining information on structure and content to automatically annotate natural science spreadsheets. Int. J. Hum.-Comput. Stud. 130, 63–76 (2017). https://doi.org/10.1016/j.ijhcs.2017.02.006

    Article  Google Scholar 

  14. Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: SIGCHI Conference on Human Factors in Computing Systems, 3363–3372 (2011). https://doi.org/10.1145/1978942.1979444

  15. Hung, V., Benatallah, B., Saint-Paul, R.: Spreadsheet-based complex data transformation. In: 20th ACM International Conference on Information and Knowledge Management, pp. 1749–1754 (2011). https://doi.org/10.1145/2063576.2063829

  16. Harris, W., Gulwani, S.: Spreadsheet table transformations from examples. ACM SIGPLAN Notices 46(6), 317–328 (2011). https://doi.org/10.1145/1993316.1993536

    Article  Google Scholar 

  17. Astrakhantsev, N., Turdakov, D., Vassilieva, N.: Semi-automatic data extraction from tables. In: Proceedings 15th All-Russian Conference Digital Libraries, pp. 14–20 (2013)

    Google Scholar 

  18. Barowy, D.W., Gulwani, S., Hart, T., Zorn, B.: FlashRelate: extracting relational data from semi-structured spreadsheets using examples. ACM SIGPLAN Notices 50(6), 218–228 (2015). https://doi.org/10.1145/2813885.2737952

    Article  Google Scholar 

  19. Cunha, J., Erwig, M., Mendes, M., Saraiva, J.: Model inference for spreadsheets. Autom. Softw. Eng. 23, 361–392 (2016). https://doi.org/10.1007/s10515-014-0167-x

    Article  Google Scholar 

  20. Jin, Z., Anderson, M.R., Cafarella, M., Jagadish, H.V.: Foofah: Transforming data by example. In: ACM International Conference Management of Data, pp. 683–698 (2017). https://doi.org/10.1145/3035918.3064034

  21. Hermans, F., Pinzger, M., van Deursen, A.: Automatically extracting class diagrams from spreadsheets. In: D’Hondt, T. (ed.) ECOOP 2010. LNCS, vol. 6183, pp. 52–75. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14107-2_4

    Chapter  Google Scholar 

  22. Amalfitano, D., Fasolino, A.R., Tramontana, P., De Simone, V., Di Mare, G., Scala, S.: A reverse engineering process for inferring data models from spreadsheet-based information systems: an automotive industrial experience. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds.) DATA 2014. CCIS, vol. 178, pp. 136–153. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25936-9_9

    Chapter  Google Scholar 

  23. Tijerino, Y.A., Embley, D.W., Lonsdale, D.W., Ding, Y., Nagy, G.: Towards ontology generation from tables. World Wide Web Internet Web Inf. Syst. 8(8), 261–285 (2005). https://doi.org/10.1007/s11280-005-0360-8

    Article  Google Scholar 

  24. Yurin A.Y., Dorodnykh N.O., Nikolaychuk O.A., Berman A.F., Pavlov A.I.: ISI models, mendeley data, v1 (2019). https://doi.org/10.17632/f9h2t766tk.1

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of the Russian Academy of Sciences, 134, Lermontov st., Irkutsk, 664033, Russia

    Nikita O. Dorodnykh, Aleksandr Yu. Yurin & Alexey O. Shigarov

Authors
  1. Nikita O. Dorodnykh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aleksandr Yu. Yurin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexey O. Shigarov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aleksandr Yu. Yurin .

Editor information

Editors and Affiliations

  1. Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian

  2. Lucian Blaga University of Sibiu, Sibiu, Romania

    Laura Florentina Stoica

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39236-9

  • Online ISBN: 978-3-030-39237-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation