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Extracting class diagram from hidden dependencies in data set

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A conceptual model is a high-level, graphical representation of a specic domain, presenting its key concepts and relationships between them. In particular, these dependencies can be inferred from concepts' instances being a part of big raw data files. The paper aims to propose a method for constructing a conceptual model from data frames encompassed in data files. The result is presented in the form of a class diagram. The method is explained with several examples and verified by a case study in which the real data sets are processed. It can also be applied for checking the quality of a data set.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
211--236
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
  • Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw
  • Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw
  • Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw
Bibliografia
  • [1] Data Cleansing: Care for most valuable business asset. https://www.hitechbpo. com/data-cleansing.php.
  • [2] Embley D., Campbell D., Jiang Y., Liddle S.W., Lonsdale D.W., Ng Y.-K., Smith R.D.: Conceptual-model-based data extraction from multiple-record Web pages, Data & Knowledge Engineering, vol. 31(3), pp. 227–251, 1999. https: //doi.org/10.1016/S0169-023X(99)00027-0.
  • [3] Embley D., Kurtz B.D., Woodfield S.N.: Object-Oriented Systems Analysis: A Model-Driven Approach. Prentice Hall, USA, 1992.
  • [4] Embley D., Liddle S.: Conceptual Modeling, chap. Big Data – Conceptual Modeling to the Rescue. Springer, Heidelberg, 2013.
  • [5] Hermans F., Pinzger M., Deursen van A.: ECOOP 2010 – Object-Oriented Programming, chap. Automatically Extracting Class Diagrams from Spreadsheets, pp. 52–75, Springer, Heidelberg, 2010.
  • [6] Hnatkowska B., Huzar Z., Tuzinkiewicz L.: Integrating research and practice in software engineering, chap. A data-driven conceptual modeling, pp. 97–109, Springer, Cham, 2020.
  • [7] Kedar S.: Database Management System. Technical Publications, USA, 2011.
  • [8] Kung C., S¨olvberg A.: Activity modeling and behavior modeling. In: Proceedings of the IFIP WG 8.1 Working Conference on Information Systems Design Methodologies: Improving the Practice, pp. 145–171, North-Holland Publishing Co., Amsterdam, 1986. http://dl.acm.org/citation.cfm?id=20143.20149.
  • [9] Liu J., Li J., Liu Ch., Chen Y.: Discover Dependencies from Data – A Review, IEEE Transactions on Knowledge and Data Engineering, vol. 24(2), pp. 251–264, 2012. http://dx.doi.org/10.1109/TKDE.2010.197.
  • [10] Ma Z.: Fuzzy Database Modeling with XML, Springer, Boston, 2005. https: //doi.org/10.1007/b104945.
  • [11] McKinney W.: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition. O’Reilly Media, USA, 2017.
  • [12] Ross R.: Conceputal Model vs. Concept Model: Not the Same!, Business Rules Journal, vol. 20, 2019. http://www.brcommunity.com/a2019/b977.html.
  • [13] Svolba G.: Data Quality for Analytics Using SAS, SAS Institute Inc., USA, 2012.
  • [14] Teixeira R., Amaral V. (2016) On the Emergence of Patterns for Spreadsheets Data Arrangements. In: P. Milazzo, D. Varr´o, M. Wimmer (eds.), Software Technologies: Applications and Foundations. STAF 2016, Lecture Notes in Computer Science, vol. 9946, pp. 333–345, Springer, Cham, 2012.
  • [15] Tijerino Y., Embley D., Lonsdale D., Ding Y.: Towards Ontology Generation from Tables, World Wide Web, vol. 8, pp. 261–285, 2005.
  • [16] Veerman E., Moss J., Knight B., Hackney J.: SQL Server 2008. Integration Services. Problem-Design-Solution. O’Reilly Media, USA, 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-136a1736-0ab5-4e1f-842c-a123f229e180
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