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A Report-Driven Approach to Design Multidimensional Models

  • Antonia AzziniEmail author
  • Stefania Marrara
  • Andrea Maurino
  • Amir Topalović
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 340)

Abstract

Today, large organisations and regulated markets are subject to the control of external audit associations, which require the submission of a huge amount of information in the form of predefined and rigidly structured reports. The compilation of these reports requires the extraction, transformation and integration of data from different heterogeneous operational databases. This task is usually performed by developing a software ad hoc for each report, or by adopting a data warehouse and analysis tools, which are now established technologies. Unfortunately, the data warehousing process is notoriously long and error prone, and is therefore particularly inefficient when the output of the data warehousing is represented by a limited number of reports. This article presents “MMBR”, an approach that can generate a multidimensional model from the structure of expected reports as data warehouse output. The approach is able to generate the multidimensional model and populate the data warehouse by defining a knowledge base specific to the domain. Although the use of semantic information in data storage is not new, the novel contribution of our approach is represented by the idea of simplifying the design phase of the data warehouse, making it more efficient, by using an industry-specific knowledge base and a report-based approach.

Keywords

Multidimensional design Knowledge base Report driven methodology 

References

  1. 1.
    Lymer, A., Debreceny, R., Gray, G.: Business Reporting on the Internet (1999)Google Scholar
  2. 2.
    Simkovic, M.: Competition and crisis in mortgage securitization. Indiana Law J. 88, 213 (2013)Google Scholar
  3. 3.
    Winter, R., Strauch, B.: A method for demand-driven information requirements analysis in data warehousing projects. In: 36th Hawaii International Conference on System Sciences (HICSS-36 2003), CD-ROM/Abstracts Proceedings, HI, USA, 6–9 January 2003, p. 231. IEEE Computer Society, Big Island (2003)Google Scholar
  4. 4.
    Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: a conceptual model for data warehouses. Int. J. Coop. Inf. Syst. 7(2–3), 215–247 (1998)CrossRefGoogle Scholar
  5. 5.
    Golfarelli, M., Graziani, S., Rizzi, S.: Starry vault: automating multidimensional modeling from data vaults. In: Pokorný, J., Ivanović, M., Thalheim, B., Šaloun, P. (eds.) ADBIS 2016. LNCS, vol. 9809, pp. 137–151. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44039-2_10CrossRefGoogle Scholar
  6. 6.
    Blanco, C., de Guzmán, I.G.R., Fernández-Medina, E., Trujillo, J.: An architecture for automatically developing secure OLAP applications from models. Inf. Softw. Technol. 59, 1–16 (2015)CrossRefGoogle Scholar
  7. 7.
    Jovanovic, P., Romero, O., Simitsis, A., Abelló, A., Mayorova, D.: A requirement-driven approach to the design and evolution of data warehouses. Inf. Syst. 44, 94–119 (2014)CrossRefGoogle Scholar
  8. 8.
    Prat, N., Akoka, J., Comyn-Wattiau, I.: A UML-based data warehouse design method. Decis. Support Syst. 42(3), 1449–1473 (2006)CrossRefGoogle Scholar
  9. 9.
    Nabli, A., Feki, J., Gargouri, F.: Automatic construction of multidimensional schema from OLAP requirements. In: 2005 ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2005), 3–6 January 2005, Egypt, p. 28. IEEE Computer Society, Cairo (2005)Google Scholar
  10. 10.
    Giorgini, P., Rizzi, S., Garzetti, M.: Grand: a goal-oriented approach to requirement analysis in data warehouses. Decis. Support Syst. 45(1), 4–21 (2008)CrossRefGoogle Scholar
  11. 11.
    Blanco, C., de Guzmán, I.G.R., Fernández-Medina, E., Trujillo, J.: An MDA approach for developing secure OLAP applications: metamodels and transformations. Comput. Sci. Inf. Syst. 12(2), 541–565 (2015)CrossRefGoogle Scholar
  12. 12.
    Bontcheva, K., Wilks, Y.: Automatic report generation from ontologies: the MIAKT approach. In: Meziane, F., Métais, E. (eds.) NLDB 2004. LNCS, vol. 3136, pp. 324–335. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27779-8_28CrossRefGoogle Scholar
  13. 13.
    Nebot, V., Berlanga, R., Pérez, J., Aramburu, M., Pedersen, T.: Multidimensional integrated ontologies: a framework for designing semantic data warehouses. J. Data Semant. XII I, 1–36 (2009)Google Scholar
  14. 14.
    Romero, O., Abelló, A.: A framework for multidimensional design of data warehouses from ontologies. Data Knowl. Eng. 69(11), 1138–1157 (2010)CrossRefGoogle Scholar
  15. 15.
    Thenmozhi, M., Vivekanandan, K.: An ontology based hybrid approach to derive multidimensional schema for data warehouse. Int. J. Comput. Appl. 54(8), 36–42 (2012)Google Scholar
  16. 16.
    Thenmozhi, M., Vivekanandan, K.: A framework to derive multidimensional schema for data warehouse using ontology. In: Proceedings of National Conference on Internet and WebSevice Computing, NCIWSC (2012)Google Scholar
  17. 17.
    Benslimane, D., Arara, A., Falquet, G., Maamar, Z., Thiran, P., Gargouri, F.: Contextual ontologies. In: Yakhno, T., Neuhold, E.J. (eds.) ADVIS 2006. LNCS, vol. 4243, pp. 168–176. Springer, Heidelberg (2006).  https://doi.org/10.1007/11890393_18CrossRefGoogle Scholar
  18. 18.
    Barkat, O., Khouri, S., Bellatreche, L., Boustia, N.: Bridging context and data warehouses through ontologies. In: Proceedings of the Symposium on Applied Computing, pp. 336–341. ACM (2017)Google Scholar
  19. 19.
    W3C: W3C Standard Consortium. http://www.w3.org
  20. 20.
    Pardillo, J., Mazón, J.N.: Using ontologies for the design of data warehouses. Int. J. Database Manag. Syst. (IJDMS) 3(2), 73–87 (2011)CrossRefGoogle Scholar
  21. 21.
    Protégé: Protégé Ontology Editor. https://protege.stanford.edu/
  22. 22.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)CrossRefGoogle Scholar
  23. 23.
    Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition. Doc. Anal. Recogn. Models Obs. Transform. Infer. 7(1), 1–16 (2004)Google Scholar
  24. 24.
    Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: a conceptual model for data warehouses. Int. J. Coop. Inf. Syst. 7(02n03), 215–247 (1998)CrossRefGoogle Scholar
  25. 25.
    Sugumaran, V., Storey, V.C.: Ontologies for conceptual modeling: their creation, use, and management. Data Knowl. Eng. 42(3), 251–271 (2002)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Antonia Azzini
    • 1
    Email author
  • Stefania Marrara
    • 1
  • Andrea Maurino
    • 2
  • Amir Topalović
    • 1
  1. 1.Consorzio per il Trasferimento Tecnologico, C2TMilanItaly
  2. 2.Dipartiment of Informatics, Systemistics and CommunicationUniversitá degli studi di Milano BicoccaMilanItaly

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