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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ross, R.G.: Conceputal model vs. concept model: not the same! Bus. Rules J. 20(1) (2019) http://www.brcommunity.com/a2019/b977.html. Cited 31 May 2019
Embley, D.W., Liddle, S.W.: Big Data—Conceptual Modeling to the Rescue. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) Conceptual Modeling, pp. 1–8. Springer, Heidelberg (2013)
Kung, C.H., Solvberg, A.: Activity modeling and behavior modeling. In: Proceedings of the IFIP WG 8.1 Working Conference on Comparative Review of Information Systems Design Methodologies: Improving the Practice, pp. 145–71. North-Holland, Amsterdam (1986)
Tijerino, Y.A., Embley, D.W., Lonsdale, D.W., et al.: World Wide Web 8, 261 (2005) https://doi.org/10.1007/s11280-005-0360-8
Liu, J., Li, J., Liu, Ch., Chen, Y.: Discover dependencies from data—a review. IEEE Trans. Knowl. Data Eng. 24(2), 251–264 (2012). https://doi.org/10.1109/TKDE.2010.197
Hermans, F., Pinzger, M., van Deursen, A.: Automatically extracting class diagrams from spreadsheets. In: D’Hondt, T. (eds) ECOOP 2010—Object-Oriented Programming. ECOOP 2010, pp. 52–75. Springer, Heidelberg (2010)
Teixeira, R., Amaral, V.: On the emergence of patterns for spreadsheets data arrangements. In: Milazzo, P., Varró, D., Wimmer, M. (eds.) Software Technologies: Applications and Foundations. STAF 2016, pp. 333–345. Springer, Cham (2016)
Embley, D.W., Campbell, D.M., Jiang, Y.S., et al.: Conceptual-model-based data extraction from multiple-record Web pages. Data Knowl. Eng. 31(3), 227–251 (1999). https://doi.org/10.1016/S0169-023X(99)00027-0
McKinney, W.: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd edn. O’Reilly Media (2017)
Svolba, G.: Data Quality for Analytics Using SAS. SAS Institute Inc. (2012)
Data Cleansing: Care for Most Valuable Business Asset. https://www.hitechbpo.com/data-cleansing.php. Cited 31 May 2019
Veerman, E., Moss, J.M., Knight, B., Hackney, J.: SQL Server 2008. Integration Services. Problem-Design-Solution. Wiley Publishing (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-26574-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26573-1
Online ISBN: 978-3-030-26574-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)