| dc.creator | Eckert, Karina | |
| dc.creator | Britos, Paola Verónica | |
| dc.date | 2019-10 | |
| dc.date | 2019 | |
| dc.date | 2020-03-17T18:42:52Z | |
| dc.date.accessioned | 2023-07-14T18:59:58Z | |
| dc.date.available | 2023-07-14T18:59:58Z | |
| dc.identifier | http://sedici.unlp.edu.ar/handle/10915/91028 | |
| dc.identifier | isbn:978-987-688-377-1 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7433599 | |
| dc.description | The amount of data currently available for Strategic Decision Making is substantial; which is why Data Science find itself in apogee in various areas where it can be applied. Expertise respecting the areas’ methodologies is fundamental; which is why, the objective of this paper is to compare and ponder them, for which, Analytic Hierarchy Process, was utilized along with linguistic tags and Personal Construction Theory, with the purpose of establishing and prioritizing characteristics according to their degree of compliance in real validation cases. The sub-criteria were grouped in different levels, conforming a hierarchy for the present problem. The validation case consisted in determining causes for breakdowns in new automobiles as they are being transported from the factory to the concessionaires; in which the proposed model proved useful and MoProPEI could be identified as the most adequate methodology. | |
| dc.description | XVI Workshop Bases de Datos y Minería de Datos. | |
| dc.description | Red de Universidades con Carreras en Informática | |
| dc.format | application/pdf | |
| dc.format | 477-486 | |
| dc.language | en | |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | |
| dc.subject | Ciencias Informáticas | |
| dc.subject | Data Science Methodologies | |
| dc.subject | Analytic Hierarchy Process | |
| dc.subject | Personal Construction Theory | |
| dc.subject | Linguistic tags | |
| dc.subject | Criteria | |
| dc.title | Data science methodologies selection with hierarchical analytical process and personal construction theory | |
| dc.type | Objeto de conferencia | |
| dc.type | Objeto de conferencia | |
