Abstract
The analysis of the data generated in manufacturing processes and products, also known as Industry 4.0, has gained a lot of popularity in last years. However, as in any data analysis process, data to be processed must be manually gathered and transformed into tabular datasets that can be digested by data analysis algorithms. This task is typically carried out by writing complex scripts in low-level data management languages, such as SQL. This task is labor-intensive, requires hiring data scientists, and hampers the participation of industrial engineers or company managers. To alleviate this problem, in a previous work, we developed Lavoisier, a language for dataset generation that focuses on what data must be selected and hides the details of how these data are transformed. To describe data available in a domain, Lavoisier relies on object-oriented data models. Nevertheless, in manufacturing settings, industrial engineers are most used to describe influences and relationships between elements of a production process by means of fishbone diagrams. To solve this issue, this work presents a model-driven process that adapts Lavoisier to work directly with fishbone diagrams.
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Notes
- 1.
If entities being analyzed required being identified by several values, a column per each one of these values would be added to this tabular structure.
References
Dave, N., Kannan, R., Chaudhury, S.K.: Analysis and prevention of rust issue in automobile industry. Int. J. Eng. Res. Technol. 4(10), 1–10 (2018)
Dziuba, S.T., Jarossová, M.A., Gołȩbiecka, N.: Applying the Ishikawa diagram in the process of improving the production of drive half-shafts. In: Borkowski, S., Ingaldi, M. (eds.) Toyotarity. Evaluation and Processes/Products Improvement, chap. 2, pp. 20–23. Aeternitas (2013)
Gwiazda, A.: Quality tools in a process of technical project management. J. Achievements Mater. Manuf. Eng. 18(1–2), 439–442 (2006)
Haverkort, B.R., Zimmermann, A.: Smart industry: how ICT will change the game! IEEE Internet Comput. 21(1), 8–10 (2017)
Ishikawa, K.: Guide to Quality Control. Asian Productivity Organization (1976)
Lee, S.M., Lee, D., Kim, Y.S.: The quality management ecosystem for predictive maintenance in the Industry 4.0 era. Int. J. Qual. Innov. 5(1), 1–11 (2019)
Lu, Y.: Industry 4.0: A survey on technologies, applications and open research issues. J. Indus. Inf. Integr. 6, 1–10 (2017)
Piekara, A., Dziuba, S., Kopeć, B.: The use of Ishikawa diagram as means of improving the quality of hydraulic nipple. In: Borkowski, S., Selejdak, J. (eds.) Toyotarity. Quality and Machines Operating Conditions, chap. 15, pp. 162–175 (2012)
Shigemitsu, M., Shinkawa, Y.: Extracting class structure based on fishbone diagrams. In: Proceedings of the 10th International Conference on Enterprise Information Systems (ICEIS), vol. 2, pp. 460–465 (2008)
Siwiec, D., Pacana, A.: The use of quality management techniques to analyse the cluster of porosities on the turbine outlet nozzle. Prod. Eng. Arch. 24(24), 33–36 (2020)
Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF: Eclipse Modeling Framework, 2 edn. Addison-Wesley Professional (2008)
Tague, N.R.: The Quality Toolbox. Rittenhouse, 2 edn. (2005)
de la Vega, A.: Domain-Specific Languages for Data Mining Democratisation. Phd thesis, Universidad de Cantabria (2019). http://hdl.handle.net/10902/16728
de la Vega, A., García-Saiz, D., Zorrilla, M., Sánchez, P.: On the automated transformation of domain models into tabular datasets. In: Proceedings of the ER Forum. CEUR Workshop Proceedings, vol. 1979, pp. 100–113 (2017)
de la Vega, A., García-Saiz, D., Zorrilla, M., Sánchez, P.: Lavoisier: A DSL for increasing the level of abstraction of data selection and formatting in data mining. J. Comput. Lang. 60, 100987 (2020)
Xu, Z., Dang, Y.: Automated digital cause-and-effect diagrams to assist causal analysis in problem-solving: a data-driven approach. Int. J. Prod. Res. 58(17), 5359–5379 (2020)
Yun, Z., Weihua, L., Yang, C.: The study of multidimensional-data flow of fishbone applied for data mining. In: Proceedings of the 7th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 86–91 (2009)
Yurin, A., Berman, A., Dorodnykh, N., Nikolaychuk, O., Pavlov, N.: Fishbone diagrams for the development of knowledge bases, In: Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) pp. 967–972 (2018)
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Funded by the Spanish Government under grant TIN2017-86520-C3-3-R.
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Sal, B., García-Saiz, D., Sánchez, P. (2021). Automated Generation of Datasets from Fishbone Diagrams. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_20
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