Abstract
Virtual social networks imply an important opportunity to generate friendlier communication bridges between students, teachers and other actors related to the educational field. In this sense, our study presents an approximation to the connection habits between university students in these networks, which in the future will allow to take advantage of these platforms to achieve a successful communication between actors. Thus, the characterization of uses, habits and consumption of virtual social networks becomes very relevant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618 (2010). http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=5326
Scheffer, T.: Finding association rules that trade support optimally against confidence. Intell. Data Anal. 9(4), 381–395 (2004)
Hernández, J.A., Burlak, G., Muñoz Arteaga, J., Ochoa, A.: Propuesta para la evaluación de objetos de aprendizaje desde una perspectiva integral usando minería de datos. In: Hernández, A., and Zechinelli, J. (eds.) Avances en la ciencia de la computación, pp. 382–387. Universidad Autónoma de México, México (2006)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, Statistics, pp. 281–297. University of California, Berkeley (1967)
Martínez, G.: Minería de datos. Cómo hallar una aguja en un pajar. Ingenierías XIV(53), 53–66 (2001)
Medina, R., Cortés, R.: El MSN como medio de comunicación y socialización entre los jóvenes de Motul, Yucatán. In: Cortés, R. (ed.) Comunicación y juventud en Yucatán. Ediciones de la Universidad Autónoma de Yucatán, Mérida (2010)
Tsiniduo, M., et al.: Evaluation of the factors that determine quality in higher education: an empirical study. Qual. Assur. Educ. 18, 227–244 (2010)
Gonzalez Espinoza, O.: Quality of higher education: concepts and models. Cal. Sup. Educ. 28, 249–296 (2008)
Bonerge Pineda Lezama, O., Varela Izquierdo, N., Pérez Fernández, D., Gómez Dorta, R.L., Viloria, A., Romero Marín, L.: Models of multivariate regression for labor accidents in different production sectors: comparative study. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_5
Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93818-9_16
Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J. Sci. Technol. 5(2), 61–75 (2017)
Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63
Chase, R.B., et al.: Administración de operaciones: producción y cadena de suministros. McGraw-Hill/Interamericana Editores, Bogota (2009)
Chen, T.-L., Su, C.-H., Cheng, C.-H., Chiang, H.-H.: A novel price-pattern detection method based on time series to forecast stock market. Afr. J. Bus. Manag. 5(13), 5188 (2011)
Conejo, A.J., Contreras, J., Espinola, R., Plazas, M.A.: Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 21(3), 435–462 (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Du, X.F., Leung, S.C.H., Zhang, J.L., Lai, K.K.: Demand forecasting of perishable farm products using support vector machine. Int. J. Syst. Sci. 44(3), 556–567 (2011)
Matich, D.J.: Redes Neuronales: Conceptos básicos y aplicaciones. Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I (2001)
Mercado, D., Pedraza, L., Martínez, E.: Comparación de Redes Neuronales aplicadas a la predicción de Series de Tiempo. Prospectiva 13(2), 88–95 (2015)
Wen, Q., Mu, W., Sun, L., Hua, S., Zhou, Z.: Daily sales forecasting for grapes by support vector machine. In: Li, D., Chen, Y. (eds.) CCTA 2013. IAICT, vol. 420, pp. 351–360. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54341-8_37
Cáceres, M., Ruiz, J., Brändle, G.: Comunicación interpersonal y vida cotidiana. La presentación de la identidad de los jóvenes en Internet. CIC: Cuadernos información comunicación 14, 213–231 (2009)
Cancelo, M., Almansa, A.: Estrategias comunicativas en redes sociales. Estudio comparativo entre las universidades de España y México. Historia Comun. Soc. 18, 423–435 (2013)
Castells, M.: La era de la información: economía, sociedad y cultura, vol. 3. Siglo XXI Editores, México (2004)
Cortés, R.: Interacción en Redes Sociales Virtuales entre estudiantes de Licenciatura. Una aproximación con fines pedagógicos. Rev. Iberoamericana Producción Académica Gestión Educativa (1) (2015). http://www.pag.org.mx/index.php/PAG/article/view/107/155
Cortés, R., Canto, P.: Usos de la red social Facebook entre estudiantes universitarios. In: Prieto, M.E., Pech, S.J., Pérez, A. (eds.) Tecnologías y aprendizaje. Avances en Iberoamérica, vol. 1, pp. 351–358. Editorial de la Universidad Técnológica de Cancún, Cancún (2013)
Wu, Q., Yan, H.S., Yang, H.B.: A forecasting model based support vector machine and particle swarm optimization. In: 2008 Workshop on Power Electronics and Intelligent Transportation System, pp. 218–222 (2008)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(Suppl. C), 159–175 (2003)
Viloria, A., Gaitan-Angulo, M.: Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J. Sci. Technol. 9(47) (2016). https://doi.org/10.17485/ijst/2016/v9i47/107371
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Silva, J. et al. (2019). Information Consumption Patterns from Big Data. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-15-1304-6_3
Download citation
DOI: https://doi.org/10.1007/978-981-15-1304-6_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1303-9
Online ISBN: 978-981-15-1304-6
eBook Packages: Computer ScienceComputer Science (R0)