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A Comprehensive Methodology to Implement Business Intelligence and Analytics Through Knowledge Discovery in Databases

  • Fernando Paulo BelfoEmail author
  • Alina Banca Andreica
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)

Abstract

Business intelligence is used by companies for analysing business information, providing not only historical or current views on business operations, but also providing predictions about the business. Consequently, knowledge discovery in databases can support the implementation of business intelligence solutions, especially in order to deal with the reality of big data, using diverse data mining techniques that can help to better prepare the data and to create improved models. The current paper proposes a methodology to implement business intelligence and analytics solutions, based on the CRISP-DM methodology, where the application of simplification and equivalence algorithms in modelling data representations can be used for improving the process of business management. This promising approach can boost business intelligence and analytics by using alternative techniques for discovering and presenting new knowledge about the business. The application of simplification and equivalence algorithms within the business context enables finding the most comprehensive or relevant knowledge, represented for instance as association rules, and bringing a real competitive advantage for the stakeholders.

Keywords

Business intelligence Knowledge discovery in databases Data mining Equivalence algorithm Canonical representation 

References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)Google Scholar
  2. 2.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. Springer, Heidelberg (1996).  https://doi.org/10.1007/978-3-319-93040-4CrossRefGoogle Scholar
  3. 3.
    Ngai, E., Hu, Y., Wong, Y., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(3), 559–569 (2011)CrossRefGoogle Scholar
  4. 4.
    Azadmanesh, S., Tarokh, M.J.: Labeling customers using discovered knowledge case study: automobile industry. Int. J. Manag. Value Supply Chain. (IJMVSC) 3(3), 13–24 (2012)CrossRefGoogle Scholar
  5. 5.
    Loureiro, A., Lourenço, J., Costa, E., Belfo, F.: Indução de Árvores de Decisão na Descoberta de Conhecimento: Caso de Empresa de Organização de Eventos. In: VI Congresso Internacional de Casos Docentes em Marketing Público e Não Lucrativo. ISCAC Business School, Coimbra, Portugal (2014)Google Scholar
  6. 6.
    Pimenta, C., Ribeiro, R., Sá, V., Belfo, F.P.: Fatores que Influenciam o Sucesso Escolar das Licenciaturas numa Instituição de Ensino Superior Portuguesa. In: 18ª Conferência da Associação Portuguesa de Sistemas de Informação (CAPSI 2018) Associação Portuguesa de Sistemas de Informação: Santarém, Portugal (2018)Google Scholar
  7. 7.
    Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artif. Intell. Med. 26(1–2), 1–24 (2002)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36, 1165–1188 (2012)CrossRefGoogle Scholar
  9. 9.
    Chapman, P., et al.: CRISP-DM 1.0: Step-By-Step Data Mining Guide. SPSS, CRISP-DM Consortium: U.S.A (2000)Google Scholar
  10. 10.
    Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the R-extension package arulesViz. R project module, pp. 223–238 (2011)Google Scholar
  11. 11.
    Galvão, N.D., Marin, H.D.F.: Data mining: a literature review. Acta Paulista de Enfermagem 22(5), 686–690 (2009)CrossRefGoogle Scholar
  12. 12.
    Berry, M.J., Linoff, G.: Data Mining Techniques: For Marketing, Sales, and Customer Support. Wiley, New York (1997)Google Scholar
  13. 13.
    Fu, Y.: Data mining: tasks, techniques, and applications. IEEE Potentials 16(4), 18–20 (1997)CrossRefGoogle Scholar
  14. 14.
    Kivikunnas, S.: Overview of process trend analysis methods and applications. In: ERUDIT Workshop on Applications in Pulp and Paper Industry. Citeseer (1998)Google Scholar
  15. 15.
    Andreica, A., Stuparu, D., Miu, C.: Design techniques in processing hierarchical structures at database level. In: Proceedings of Iadis Information Systems, pp. 483–488 (2010)Google Scholar
  16. 16.
    Andreica, A., Stuparu, D., Miu, C.: Applying mathematical models in software design. In: 2012 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE (2012)Google Scholar
  17. 17.
    Buchberger, B., Loos, R.: Algebraic simplification. In: Buchberger, B., Collins, G.E., Loos, R., Albrecht, R. (eds.) Computer Algebra, pp. 11–43. Springer, Vienna (1982).  https://doi.org/10.1007/978-3-7091-7551-4_2CrossRefGoogle Scholar
  18. 18.
    Andreica, A.: Designing uniform database representations for cloud data interchange services. In: Proceedings of CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science, pp. 554–559 (2017)Google Scholar
  19. 19.
    Andreica, A.: Applying Equivalence Algorithms in Solving Pattern Matching Problems. Case Study for Expert System Design, p. 255. ICT, Society, and Human Beings (2016)Google Scholar
  20. 20.
    Andreica, A., Belfo, F.: Building cloud data interchange services for E-learning systems: applications on the moodle system. In: Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018), pp. 565–572 (2018)Google Scholar
  21. 21.
    Reich, B.H., Benbasat, I.: Measuring the linkage between business and information technology objectives. MIS Q. 20(1), 55–81 (1996)CrossRefGoogle Scholar
  22. 22.
    Belfo, F., Sousa, R.D.: Reviewing business-IT alignment instruments under SAM dimensions. Int. J. Inf. Commun. Technol. Hum. Dev. 5(3), 18–40 (2013)CrossRefGoogle Scholar
  23. 23.
    Kappelman, L., et al.: The 2016 SIM IT Issues and Trends Study. MIS Q. Exec. 16(1), 47–80 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Polytechnic Institute of Coimbra, ISCAC Coimbra Business SchoolCoimbraPortugal
  2. 2.Faculty of European StudiesBabes-Bolyai University of Cluj-NapocaCluj-NapocaRomania

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