Article Sidebar
Foreign trade is fundamental to the economy as it involves many transactions and data from a country. This study analyses exports from Ecuador to generate knowledge for experts in this field. A data visualization model covering 2008 and 2018 was developed to achieve this. The study was based on the CRISP-DM methodology, widely used in data mining projects. This methodology is adapted through five iterative phases to develop the mining and visualization model. Through the implementation of this methodology, it was possible to generate a visual model that provides an effective tool to manipulate the main variables related to foreign trade. The result was the creation of a graphical model adapted into a tool to explore and analyse Ecuador's exports. This model provides an intuitive interface to manipulate and examine the main variables, which can facilitate informed decision-making in foreign trade.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Boar, R., Iovanovici, A., y Ciocarlie, H. (2017). Complex networks analysis of international import-export trade. 2017 IEEE 14th International Scientific Conference on Informatics, 31-34. https://doi.org/10.1109/INFORMATICS.2017.8327217
Cairo, A. (2011). El arte de lo funcional: infografía y visualización de información. Alamut.
Devyatkin, D., Suvorov, R., Tikhomirov, I., y Otmakhova, Y. (2018). Neural Networks for Food Export Gain Forecasting. 2018 International Conference on Intelligent Systems (IS), 312-317. https://doi.org/10.1109/IS.2018.8710561
Hui, Z. (2018). Analysis of International Marketing Strategy Based on Intelligent Mining Algorithm for Big Data. 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA), 261-264. https://doi.org/10.1109/ICICTA.2018.00065
Jiang, H., Shen, J., Chou, Q., Dong, Z., y Cheng, S. (2021). Visual Analytics for the International Trade. 2021 5th International Conference on Vision, Image and Signal Processing (ICVISP), 296-301. https://doi.org/10.1109/ICVISP54630.2021.00059
Jiang, M., Niu, L., Zhang, Y., Wang, Z., y Ren, X. (2018). A Big-Data-Analysis-Based Research Study on the Changes Both in the Dependence on Foreign Trade and in the Trade Structure (2000-2015) of the Yunnan Province of China. 2018 International Conference on Intelligent Transportation, Big Data y Smart City (ICITBS), 235-239. https://doi.org/10.1109/ICITBS.2018.00068
Krugman, P., Obstfeld, M., y Melitz, M. J. (2001). Economía internacional: Teoría y política. Pearson education Madrid.
OpenStreetMap. (s. f.). OSM. Recuperado 26 de junio de 2023, de https://www.openstreetmap.org/#map=7/-1.783/-78.132
Oyelade, J., Isewon, I., Oladipupo, O., Emebo, O., Omogbadegun, Z., Aromolaran, O., Uwoghiren, E., Olaniyan, D., y Olawole, O. (2019). Data Clustering: Algorithms and Its Applications. 2019 19th International Conference on Computational Science and Its Applications (ICCSA), 71-81. https://doi.org/10.1109/ICCSA.2019.000-1
Po, L., Bikakis, N., Desimoni, F., y Papastefanatos, G. (2020). Linked data visualization: techniques, tools, and big data. Synthesis Lectures on Semantic Web: Theory and Technology, 10(1), 1-157.
Vásquez Bernal, J. V, y Tonon Ordóñez, L. B. (2021). Modelo de gravedad de las exportaciones de cacao en grano del Ecuador. INNOVA Research Journal.
Velichko, A., Gribova, V., y Fedorishchev, L. (2018). Simulation Software for Multicommodity Flows Model of Interregional Trade. 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC), 1-5. https://doi.org/10.1109/RPC.2018.8482140