A Proposal of Effort Estimation Method for Information Mining Projects Oriented to SMEs

  • Pablo Pytel
  • Paola Britos
  • Ramón García-Martínez
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 139)


Software projects need to predict the cost and effort with its associated quantity of resources at the beginning of every project. Information Mining projects are not an exception to this requirement, particularly when they are required by Small and Medium-sized Enterprises (SMEs). An existing Information Mining projects estimation method is not reliable for small-sized projects because it tends to overestimates the estimated efforts. Therefore, considering the characteristics of these projects developed with the CRISP-DM methodology, an estimation method oriented to SMEs is proposed in this paper. First, the main features of SMEs’ projects are described and applied as cost drivers of the new method with the corresponding formula. Then this is validated by comparing its results to the existing estimation method using SMEs real projects. As a result, it can be seen that the proposed method produces a more accurate estimation than the existing estimation method for small-sized projects.


Effort Estimation method Information Mining Small and Mediumsized Enterprises Project Planning Software Engineering 


  1. 1.
    Schiefer, J., Jeng, J., Kapoor, S., Chowdhary, P.: Process Information Factory: A Data Management Approach for Enhancing Business Process Intelligence. In: Proceedings 2004 IEEE International Conference on E-Commerce Technology, pp. 162–169 (2004)Google Scholar
  2. 2.
    Stefanovic, N., Majstorovic, V., Stefanovic, D.: Supply Chain Business Intelligence Model. In: Proceedings 13th International Conference on Life Cycle Engineering, pp. 613–618 (2006)Google Scholar
  3. 3.
    Curtis, B., Kellner, M., Over, J.: Process Modelling. Communications of the ACM 35(9), 75–90 (1992)CrossRefGoogle Scholar
  4. 4.
    Ferreira, J., Takai, O., Pu, C.: Integration of Business Processes with Autonomous Information Systems: A Case Study in Government Services. In: Proceedings Seventh IEEE International Conference on E-Commerce Technology, pp. 471–474 (2005)Google Scholar
  5. 5.
    Garcia-Martinez, R., Britos, P., Pollo-Cattaneo, F., Rodriguez, D., Pytel, P.: Information Mining Processes Based on Intelligent Systems. In: Proceedings of II International Congress on Computer Science and Informatics (INFONOR-CHILE 2011), pp. 87–94 (2011) ISBN 978-956-7701-03-2Google Scholar
  6. 6.
    García-Martínez, R., Britos, P., Pesado, P., Bertone, R., Pollo-Cattaneo, F., Rodríguez, D., Pytel, P., Vanrell, J.: Towards an Information Mining Engineering. En Software Engineering, Methods, Modeling and Teaching. Sello Editorial Universidad de Medellín, pp. 83–99 (2011) ISBN 978-958-8692-32-6Google Scholar
  7. 7.
    Rodríguez, D., Pollo-Cattaneo, F., Britos, P., García-Martínez, R.: Estimación Empírica de Carga de Trabajo en Proyectos de Explotación de Información. Anales del XVI Congreso Argentino de Ciencias de la Computación, pp. 664–673 (2010) ISBN 978-950-9474-49-9Google Scholar
  8. 8.
    Boehm, B., Abts, C., Brown, A., Chulani, S., Clark, B., Horowitz, E., Madachy, R., Reifer, D., Steece, B.: Software Cost Estimation with COCOMO II. Prentice-Hall, Englewood Cliffs (2000)Google Scholar
  9. 9.
    Marbán, O., Menasalvas, E., Fernández-Baizán, C.: A cost model to estimate the effort of data mining projects (DMCoMo). Information Systems 33, 133–150 (2008)CrossRefGoogle Scholar
  10. 10.
    Pytel, P., Tomasello, M., Rodríguez, D., Pollo-Cattaneo, F., Britos, P., García-Martínez, R.: Estudio del Modelo Paramétrico DMCoMo de Estimación de Proyectos de Explotación de Información. In: Proceedings XVII Congreso Argentino de Ciencias de la Computación, pp. 979–988 (2011) ISBN 978-950-34-0756-1Google Scholar
  11. 11.
    García-Martínez, R., Lelli, R., Merlino, H., Cornachia, L., Rodriguez, D., Pytel, P., Arboleya, H.: Ingeniería de Proyectos de Explotación de Información para PYMES. In: Proceedings XIII Workshop de Investigadores en Ciencias de la Computación, pp. 253–257 (2011) ISBN 978-950-673-892-1Google Scholar
  12. 12.
    Organization for Economic Cooperation and Development: OECD SME and Entrepreneurship Outlook 2005. OECD Publishing (2005), doi: 10.1787/9789264009257-enGoogle Scholar
  13. 13.
    Álvarez, M., Durán, J.: Manual de la Micro, Pequeña y Mediana Empresa. Una contribución a la mejora de los sistemas de información y el desarrollo de las políticas públicas, CEPAL - Naciones Unidas, San Salvador (2009),
  14. 14.
    International Organization for Standardization: ISO/IEC DTR 29110-1 Software Engineering - Lifecycle Profiles for Very Small Entities (VSEs) - Part 1: Overview. International Organization for Standardization (ISO), Geneva, Switzerland (2011)Google Scholar
  15. 15.
    Laporte, C., Alexandre, S.Y., Renault, A.: Developing International Standards for VSEs. IEEE Computer 41(3), 98–101 (2008)CrossRefGoogle Scholar
  16. 16.
    Ríos, M.D.: El Pequeño Empresario en ALC, las TIC y el Comercio Electrónico. Instituto para la Conectividad en las Américas (2006),
  17. 17.
    Chapman, P., Clinton, J., Keber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step by step BI guide Edited by SPSS (2000),
  18. 18.
    Chen, Z., Menzies, T., Port, D., Boehm, D.: Finding the right data for software cost modeling. IEEE Software 22(6), 38–46 (2005), doi:10.1109/MS.2005.151CrossRefGoogle Scholar
  19. 19.
    Domingos, P., Elkan, C., Gehrke, J., Han, J., Heckerman, D., Keim, D., et al.: 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making 5(4), 597–604 (2006)CrossRefGoogle Scholar
  20. 20.
    Weisberg, S.: Applied Linear Regression. John Wiley & Sons, New York (1985)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Pablo Pytel
    • 1
    • 2
    • 3
  • Paola Britos
    • 4
  • Ramón García-Martínez
    • 2
  1. 1.PhD Program on Computer Science, Computer Science SchoolNational University of La PlataBuenos AiresArgentina
  2. 2.Information Systems Research GroupNational University of LanusBuenos AiresArgentina
  3. 3.Information System Methodologies Research GroupTechnological National University at Buenos AiresArgentina
  4. 4.Information Mining Research GroupNational University of Rio Negro at El BolsonRío NegroArgentina

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