Data Requirements Elicitation in Big Data Warehousing

  • António A. C. VieiraEmail author
  • Luís Pedro
  • Maribel Yasmina Santos
  • João Miguel Fernandes
  • Luís S. Dias
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 341)


Due to the complex and dynamic nature of Supply Chains (SCs), companies require solutions that integrate their Big Data sets and allow Big Data Analytics, ensuring that proactive measures are taken, instead of reactive ones. This paper proposes a proof-of-concept of a Big Data Warehouse (BDW) being developed at a company of the automotive industry and contributes to the state-of-the-art with the data requirements elicitation methodology that was applied, due to the lack of existing approaches in literature. The proposed methodology integrates goal-driven, user-driven and data-driven approaches in the data requirements elicitation of a BDW, complementing these different organizational views in the identification of the relevant data for supporting the decision-making process.


Big Data Big Data Warehouse Analytics Data Warehousing Hive Requirements Industry 4.0 



This work is supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013; by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 002814; Funding Reference: POCI-01-0247-FEDER-002814] and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH).


  1. 1.
    Levi, D.S., Kaminsky, P., Levi, E.S.: Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill, New York City (2003)Google Scholar
  2. 2.
    Santos, M.Y., et al.: A Big Data system supporting Bosch Braga Industry 4.0 strategy. Int. J. Inf. Manag. 37(6), 750–760 (2017)CrossRefGoogle Scholar
  3. 3.
    Ponis, S.T., Ntalla, A.C.: Supply chain risk management frameworks and models: a review. Int. J. Supply Chain Manag. 5(4), 1–11 (2016)Google Scholar
  4. 4.
    Kache, F., Seuring, S.: Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. Int. J. Oper. Prod. Manag. 37(1), 10–36 (2017)CrossRefGoogle Scholar
  5. 5.
    Tiwari, S., Wee, H., Daryanto, Y.: Big data analytics in supply chain management between 2010 and 2016: insights to industries. Comput. Ind. Eng. 115, 319–330 (2018)CrossRefGoogle Scholar
  6. 6.
    Sanders, N.R.: How to use big data to drive your supply chain. Calif. Manag. Rev. 58(3), 26–48 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhong, R.Y., Newman, S.T., Huang, G.Q., Lan, S.: Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput. Ind. Eng. 101, 572–591 (2016)CrossRefGoogle Scholar
  8. 8.
    Chen, D.Q., Preston, D.S., Swink, M.: How the use of big data analytics affects value creation in supply chain management. J. Manag. Inf. Syst. 32(4), 4–39 (2015)CrossRefGoogle Scholar
  9. 9.
    Ivanov, D.: Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. Int. J. Integr. Supply Manag. 11(1), 24–43 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kimball, R.: The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouse, vol. 248, no. 4. Willey, New York (1996)Google Scholar
  11. 11.
    Santos, M.Y., Costa, C.: Data warehousing in big data: from multidimensional to tabular data models. In: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, pp. 51–60 (2016)Google Scholar
  12. 12.
    Costa, E., Costa, C., Santos, M.Y.: Efficient Big Data modelling and organization for Hadoop hive-based data warehouses. In: Themistocleous, M., Morabito, V. (eds.) EMCIS 2017. LNBIP, vol. 299, pp. 3–16. Springer, Cham (2017). Scholar
  13. 13.
    Santos, M.Y., Costa, C.: Data models in NoSQL databases for big data contexts. In: Tan, Y., Shi, Y. (eds.) International Conference on Data Mining and Big Data, vol. 9714, pp. 475–485. Springer, Cham (2016). Scholar
  14. 14.
    Inmon, W.H.: Building the Data Warehouse. Wiley, Hoboken (2005)Google Scholar
  15. 15.
    Golfarelli, M.: From user requirements to conceptual design in data warehouse design. IGI Global (2010)Google Scholar
  16. 16.
    Abai, N.H.Z., Yahaya, J.H., Deraman, A.: User requirement analysis in data warehouse design: a review. Procedia Technol. 11, 801–806 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ALGORITMI Research CentreUniversity of MinhoBragaPortugal
  2. 2.University of MinhoBragaPortugal
  3. 3.University of MinhoGuimarãesPortugal

Personalised recommendations