Examinando por Materia "Data analytics"
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Ítem Analytics product line : a conceptual proposal for higher education institutions(Universidad EAFIT, 2021) Cárcamo Correa, Jason; Tabares Betancur, Marta SilviaÍtem FAVO: Framework de gestión autónoma de organizaciones virtuales basado en la industria 4.0(Associacao Iberica de Sistemas e Tecnologias de Informacao, 2020-01-01) Departamento de Humanidades; Estudios de Filosofía, Hermenéutica y NarrativasIn the information age, the digitalization is transforming traditional organizations, allowing them to improve their productivity, be more competitive and flexible. In addition, it gives them the opportunity to form strategic alliances, also called Virtual Organization (VO). Organizations have to face these challenges smartly, in order to collaborate effectively and achieve their goals. The existing literature shows that the different aspects of collaboration between VOs have been widely discussed and addressed, but no framework has been found that, based on the benefits of Industry 4.0, can be applied to the creation and management of VOs in a way autonomous. From this gap found in the literature, it is proposed to design a framework that guarantees the effectiveness of inter-organizational autonomous management, through the use of autonomous cycles of data analytics tasks and collaborative processes. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.Ítem FAVO: Framework de gestión autónoma de organizaciones virtuales basado en la industria 4.0(Associacao Iberica de Sistemas e Tecnologias de Informacao, 2020-01-01) Lopez, C.-P.; Santorum, M.; Aguilar, J.; Lopez, C.-P.; Santorum, M.; Aguilar, J.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las ComunicacionesIn the information age, the digitalization is transforming traditional organizations, allowing them to improve their productivity, be more competitive and flexible. In addition, it gives them the opportunity to form strategic alliances, also called Virtual Organization (VO). Organizations have to face these challenges smartly, in order to collaborate effectively and achieve their goals. The existing literature shows that the different aspects of collaboration between VOs have been widely discussed and addressed, but no framework has been found that, based on the benefits of Industry 4.0, can be applied to the creation and management of VOs in a way autonomous. From this gap found in the literature, it is proposed to design a framework that guarantees the effectiveness of inter-organizational autonomous management, through the use of autonomous cycles of data analytics tasks and collaborative processes. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.Ítem Intelligent model for monitoring, evaluating, and recommending strategies to improve the innovation processes of MSMEs(Universidad EAFIT, 2024) Gutiérrez Buitrago, Ana Gissel; Aguilar Castro, José Lisandro; Montoya Múnera, Edwin Nelson; Ortega Álvarez, Ana MaríaThe research focuses on how to improve the innovation process in micro, small and medium-sized enterprises (MSMEs). The study is framed within the Smart Innovation paradigm. In this context, innovation is considered a relevant factor for organizational performance that allows the creation and improvement of competitive advantages through the implementation of new ideas, products, concepts, and services to increase market positioning. For organizations aiming to enhance innovation performance, using intelligent systems and artificial intelligence to guide the innovation process poses a challenge. To address this problem, the goal was to develop methodologies, models and approaches to support decision-making related to the intelligent management of the innovation process. To achieve this, specific objectives were defined. The first one is to design an intelligent model to support innovation processes in MSMEs. The second objective is to apply Artificial Intelligence (AI) techniques to customer data sources in social networks and organizational data of MSMEs, aiming to enhance the innovation process; The third objective is to develop an intelligent system to evaluate the innovation levels in MSMEs. The fourth objective is to instantiate a case study in the fashion cluster of the department of Norte de Santander and in the national context, as part of the applied methodology. To fulfill these objectives, research articles were developed. The process began with a literature review article on the current challenges in applying AI techniques to improve innovation processes in MSMEs. A proposed innovation model was made based on the different innovation models that exist in the literature, and the four research articles were written in compliance with the scientific standards that accredit them, to meet the specific objectives outlined in this doctoral thesis. Each article evaluated the strategies/models using various data sets. The results demonstrated the capacity of the proposed methodologies and models for managing of innovation processes. For instance, the proposals enable the prediction of the level of innovation, and the definition of innovation problems, among other aspects, with positive results in performance metrics.Ítem Optimización de un portafolio de inversión con acciones del Colcap aplicando técnicas de machine learning(Universidad EAFIT, 2024) Osorio Buitrón, Maribel; Rico Villareal, Juan David; Rojas Ormanza, Bryan RicardoBased on the increases in the volumes of historical information on the shares of public companies, the question arises as to whether it is possible to create better optimized portfolios than those generated from traditional theory, making use of recent innovations in artificial intelligence and analysis of data from the last decade. For this reason, the present research aims to compare the traditional theory of portfolio optimization with the recent data analysis methodologies applied to Colcap. The methodology used is based on twelve previous investigations which had tested and demonstrated the performance of different machine learning models on stock exchanges around the world, such as S&P500, NASDAQ, DAX, SET and Colcap. From here, the best prospects were selected and applied to Colcap shares, to predict the future movement in the share price based on the historical behavior of certain significant variables and then compared with the traditional methodology. It was found that the best prediction model in the price movement applied to Colcap is the Random Forest, and the variables that best explain the future changes in the price of the shares of this exchange are the closing price of the share, the TRM index and the Colcap index. In addition, machine learning models managed to optimize portfolios with a smaller number of shares and higher returns backed by historical information.