Requirements Elicitation in Data Mining for Business Intelligence Projects

  • Paola Britos
  • Oscar Dieste
  • Ramón García-Martínez
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 274)


There are data mining methodologies for business intelligence (DM-BI) projects that highlight the importance of planning an ordered, documented, consistent and traceable requirement’s elicitation throughout the entire project. However, the classical software engineering approach is not completely suitable for DM-BI projects because it neglects the requirements specification aspects of projects. This article focuses on identifying concepts for understand DM-BI project domain from DM-BI field experience, including how requirements can be educed by a proposed DM-BI project requirements elicitation process and how they can be documented by a template set.


General Task Business Intelligence Requirement Engineer Contingency Plan Requirement Elicitation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Chapman P, Clinton J, Keber R, Khabaza T, Reinartz T, Shearer C, Wirth R (2000) CRISP-DM 1.0 Step by step BIguide Edited by SPSS. Accessed 14 September 2006.
  2. Cogliati M, Britos P, García-Martínez R (2006) Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT In: Bramer M (ed) Artificial Intelligence in Theory and Practice, Boston, Springer, IFIP Series 217:305-314CrossRefGoogle Scholar
  3. Felgaer P, Britos P, and García-Martínez R, (2006) Prediction in Health Domain Using Bayesian Network Optimization Based on Induction Learning Techniques. Int. J. of Mod. Ph. C 17(3): 447-455zbMATHCrossRefGoogle Scholar
  4. Grosser H, Britos P, García-Martínez R (2005) Detecting Fraud in Mobile Telephony Using Neural Networks. LNAI 3533:613-615Google Scholar
  5. IEEE (1993) Standard IEEE 830-1993: Recommended Practice for Software Requirements Specifications. Institute of Electronic and Electrical Engineers Press.Google Scholar
  6. IEEE (2004) Guide to the Software Engineering Body of Knowledge. IEEE Comp. Society Press Jiang L, Eberlein A (2007) Selecting Requirements Engineering Techniques based on Project Attributes - A Case Study. 14th Annual IEEE ECBS: 269-278Google Scholar
  7. Maiden N, Robertson S, Gizikis A (2004) Provoking Creativity: Imagine What Your Requirements Could be Like. IEEE Software 21(5): 68-75CrossRefGoogle Scholar
  8. Maiden N, Ncube C, Robertson S (2007) Can Requirements Be Creative? Experiences with an Enhanced Air Space Management System Proceedings 29th ICSE: 632-641Google Scholar
  9. Pyle D (2003) Business Modeling and Business intelligence. Morgan KaufmannGoogle Scholar
  10. SAS (2008) SAS Enterprise Miner: SEMMA datamining/ miner/semma.html. Accessed 29 February 2008
  11. Silva F, Freire J (2003) DWARF: An Approach for Requirements Definition and Management of Data Warehouse Systems. RE'03: 75-84Google Scholar
  12. Solheim H, Lillehagen F, Petersen S, Jorgensen H, Anastasiou M (2005) Model-driven visual requirements engineering Proceedings RE'05:421-428Google Scholar
  13. Valenga F, Fernández E, Merlino H, Rodríguez D, Procopio C, Britos P, García-Martínez R (2008) Minería de Datos Aplicada a la Detección de Patrones Delictivos en Argentina. VII JIISIC'08: 31-39Google Scholar
  14. Winter R, Strauch B (2002) A Method for Demand-driven Information Requirements Analysis in Data Warehousing Projects. HICSS-36:231-239Google Scholar
  15. Yang Q, Wu X (2006) 10 Challenging Problems in Data Mining Research. Int. J. Inf. Tech. & Decis. Mak. 5(4):597-604CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2008

Authors and Affiliations

  • Paola Britos
    • 1
  • Oscar Dieste
    • 2
  • Ramón García-Martínez
    • 3
  1. 1.Software and Knowledge Engineering CenterBuenos Aires Institute of TechnologyAR
  2. 2.Empirical Software Engineering Research GroupPolytechnic University of MadridES
  3. 3.Intelligent Systems Laboratory. Engineering SchoolUniversity of Buenos AiresAR

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