Proposed extended analytic hierarchical process for selecting data science methodologies




Criteria, Linguistic Labels, Data Science Methodologies, Analytic Hierarchic Process, Personal Construction Theory.


Decision making can present a considerable amount of complexity in competitive environments; where methods that support possess great relevance. The article presents an extension of the Hierarchic Analytical Process; complemented with Personal Construct Theory, which purpose is to reduce ambiguity when defining and establishing values for the criteria in a determined problem. In recent years, the scope for decision making based on data has considerably raised, which is why Data Science as a scientific field is rising in popularity; where one of the main activities for data scientists is selecting an adequate methodology to guide a project with this traits. The steps defined in the proposed model guide this task, from establishing and prioritizing criteria based on degrees of compliance, grouping them by levels, completing the hierarchical structure of the problem, performing the correct comparisons through different levels in an ascendant manner, to finally obtaining the definitive priorities of each methodology for each validation case and sorting them by their adequacy percentages. Both disparate cases, one referred to an industrial/commercial field and the other to an academic field, were effective to corroborate the extent of usefulness of the proposed model; for which in both cases MoProPEI obtained the best results.


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M. Karanik, S. Gramajo, L. Wanderer, M. Giménez, and D. Carpintero, “Multi-Criteria Decision Model based on AHP and Linguistic Information,” Journal of Computer Science & Technology, vol. 14, no. 1, pp. 16–24, Apr. 2014.

J. C. Osorio Gómez and J. P. Orejuela Cabrera, “El proceso de análisis jerárquico (AHP) y la toma de decisiones multicriterio. Ejemplo de aplicación.,” Scientia et technica, vol. 2, no. 39, Aug. 2008.

A. Dadda and I. Ouhbi, “A decision support system for renewable energy plant projects,” presented at the 2014 International Conference on Next Generation Networks and Services (NGNS), Casablanca, Morocco, 2014, pp. 356–362.

E. Triantaphyllou and S. H. Mann, “Using the analytic hierarchy process for decision making in engineering applications: Some challenges,” International Journal of Industrial Engineering: Applications and Practice, vol. 2, no. 1, pp. 35–44, Jan. 1995.

T. L. Saaty, The analytic hierarchy process. New York: McGraw-Hill, 1980.

M. del S. García Cascales, “Métodos para la comparación de alternativas mediante un Sistema de Ayuda a la Decisión (S.A.D.) y ‘Soft Computing,’” Tesis de Doctorado, Universidad Politécnica de Cartagena - Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Cartagena, Colombia, 2009.

R. de F. S. M. Russo and R. Camanho, “Criteria in AHP: A Systematic Review of Literature,” Procedia Computer Science, vol. 55, pp. 1123–1132, Jan. 2015.

G. Kou and C. Lin, “A cosine maximization method for the priority vector derivation in AHP,” European Journal of Operational Research, vol. 235, no. 1, pp. 225–232, May 2014.

E. H. Forman and S. I. Gass, “The Analytic Hierarchy Process—An Exposition,” Operations Research, vol. 49, no. 4, pp. 469–486, Aug. 2001.

O. S. Vaidya and S. Kumar, “Analytic hierarchy process: An overview of applications,” European Journal of Operational Research, vol. 169, no. 1, pp. 1–29, Feb. 2006.

J. Mayor, S. Botero, and J. D. González-Ruiz, “Modelo de decisión multicriterio difuso para la selección de contratistas en proyectos de infraestructura: caso Colombia,” Obras y proyectos, no. 20, pp. 56–74, Dec. 2016.

G. Islei and A. G. Lockett, “Judgemental modelling based on geometric least square,” European Journal of Operational Research, vol. 36, no. 1, pp. 27–35, Jul. 1988.

P. Britos, B. Rossi, and R. García Martínez, “Notas sobre didáctica de las etapas de formalización y análisis de resultados de la técnica de emparrillado. Un Ejemplo,” in Proceedings del V Congreso Internacional de Ingeniería Informática, 1999, pp. 200–209.

K. Eckert and P. V. Britos, “Modelo basado en la toma decisiones con criterios múltiples para la elección de metodologías de data science,” presented at the XX Workshop de Investigadores en Ciencias de la Computación, 2018.

K. B. Eckert and P. V. Britos, “Data science methodologies selection with hierarchical analytical process and personal construction theory,” presented at the XXV Congreso Argentino de Ciencias de la Computación (CACIC), Río Cuarto, Córdoba, Argentina, 2019.

M. A. Waller and S. E. Fawcett, “Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77–84, 2013.

P. Pytel, P. Britos, and R. García Martínez, “Proposal and Validation of a feasibility Model for Information Mining Projects,” presented at the 25th International Conference on Software Engineering and Knowledge Engineering, Boston, USA, pp. 33–88.

J. Á. Vanrell, R. A. Bertone, and R. García Martínez, “Modelo de proceso de operación para proyectos de explotación de información,” presented at the XVI Congreso Argentino de Ciencias de la Computación, 2010.

D. Pyle, Business Modeling and Data Mining, 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2003.

P. Chapman et al., “CRISP-DM 1.0: Step-by-Step Data Mining Guide.” Edited by SPSS, 2000.

S. Martins, P. Pesado, and R. García Martínez, “Propuesta de Modelo de Procesos para una Ingeniería de Explotación de Información: MoProPEI,” Revista Latinoamericana de Ingenieria de Software, vol. 2, no. 5, pp. 313–332, 2014.

T. L. Saaty, “How to make a decision: The analytic hierarchy process,” European Journal of Operational Research, vol. 48, no. 1, pp. 9–26, Sep. 1990.

T. L. Saaty, “Analytic Hierarchy Process,” in Encyclopedia of Operations Research and Management Science, S. I. Gass and M. C. Fu, Eds. Boston, MA: Springer US, 2013, pp. 52–64.

P. T. Harker, “The Art and Science of Decision Making: The Analytic Hierarchy Process,” in The Analytic Hierarchy Process: Applications and Studies, B. L. Golden, E. A. Wasil, and P. T. Harker, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989, pp. 3–36.

L. Vera Montenegro, “Aplicación y Comparación de Metodologías Multicriterio (AHP y Fuzzy Logic) en la Selección de Tecnologías Postcosecha para Pequeños Productores de Cacao,” Tesis de Doctorado, Universidad Politécnica de Valencia, Valencia, España, 2014.

T. L. Saaty, “Decision making with the analytic hierarchy process,” International Journal of Services Sciences, vol. 1, no. 1, pp. 83–98, Jan. 2008.

T. L. Saaty, Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, 2000.

R. García Martínez and P. V. Britos, Ingenieria de Sistemas Expertos. Nueva Librería, 2004.

T. Butt, George Kelly: The Psychology of Personal Constructs. Macmillan International Higher Education, 2008.

F. Provost and T. Fawcett, “Data Science and its Relationship to Big Data and Data-Driven Decision Making,” Big Data, vol. 1, no. 1, pp. 51–59, Feb. 2013.

T. Schoenherr and C. Speier‐Pero, “Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential,” Journal of Business Logistics, vol. 36, no. 1, pp. 120–132, 2015.

J. C. Giraldo Mejia and J. A. Jiménez Builes, “Caracterización del proceso de obtención de conocimiento y algunas metodologías para crear proyectos de minería de datos,” Revista Latinoamericana de Ingeniería de Software, 2013.

J. M. Moine, S. E. Gordillo, and A. S. Haedo, “Análisis comparativo de metodologías para la gestión de proyectos de minería de datos,” presented at the XVII Congreso Argentino de Ciencias de la Computación, 2011.

J. M. Moine, “Metodologías para el descubrimiento de conocimiento en bases de datos: un estudio comparativo,” Tesis de Maestría, Facultad de Informática, 2013.

H. J. G. Palacios, G. A. H. Pantoja, A. A. M. Navarro, I. M. A. Puetaman, and R. A. J. Toledo, “Comparative between CRISP-DM and SEMMA for data cleaning of MODIS products in a study of land use and land cover change,” in 2016 IEEE 11th Colombian Computing Conference (CCC), 2016, pp. 1–9.

M. T. Rodríguez Montequín, J. V. Álvarez Cabal, J. M. Mesa Fernández, and A. González Valdés, “Metodologías para la realización de proyectos de Data Mining,” presented at the VII Congreso Internacional de Ingeniería de Proyectos, Pamplona España, 2003, pp. 257–265. Journal of Computer Science & Technology, Volume 21, Number 1, April 2021 - 57




How to Cite

Eckert, K. B., & Britos, P. V. (2021). Proposed extended analytic hierarchical process for selecting data science methodologies. Journal of Computer Science and Technology, 21(1), e6.



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