Abstract
A conceptual model is a high-level, usually graphical representation of key elements of some target problem. It is especially helpful in understanding existing dependencies among domain entities. In particular, these dependencies can be described by big raw data files, and the conceptual model can be inferred from such files. The aim of the paper is to propose a method for constructing a conceptual model discovered from data frames encompassed in data files. The proposed method, based on functional dependencies among analyzed data, gathers identified properties into classes and finds relationships among them. The data used are assumed to be clean. The method is demonstrated by a simple case study in which the real data sets are processed. It is also shown how obtained conceptual model substantially depends on the input data quality. The proposed method can be applied for both discovering existing relationships among entities as well as for checking the quality of the data describing a specific domain.
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Hnatkowska, B., Huzar, Z., Tuzinkiewicz, L. (2020). A Data-Driven Conceptual Modeling. In: Jarzabek, S., Poniszewska-Marańda, A., Madeyski, L. (eds) Integrating Research and Practice in Software Engineering. Studies in Computational Intelligence, vol 851. Springer, Cham. https://doi.org/10.1007/978-3-030-26574-8_8
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DOI: https://doi.org/10.1007/978-3-030-26574-8_8
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