Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis
In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictiv...
- Autores:
-
Silva, Jesús
Hernández, Lissette
Varela, Noel
Pineda Lezama, Omar Bonerge
Tafur Cabrera, Jorge
Lucena León Castro, Bellanith Ruth
Redondo Bilbao, Osman
Pérez Coronel, Leidy
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5132
- Acceso en línea:
- http://hdl.handle.net/11323/5132
https://repositorio.cuc.edu.co/
- Palabra clave:
- Intelligent data retrieval
Data Warehouse
Unique Identification Number
Academic performance
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| title |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| spellingShingle |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis Intelligent data retrieval Data Warehouse Unique Identification Number Academic performance |
| title_short |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| title_full |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| title_fullStr |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| title_full_unstemmed |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| title_sort |
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis |
| dc.creator.fl_str_mv |
Silva, Jesús Hernández, Lissette Varela, Noel Pineda Lezama, Omar Bonerge Tafur Cabrera, Jorge Lucena León Castro, Bellanith Ruth Redondo Bilbao, Osman Pérez Coronel, Leidy |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Hernández, Lissette Varela, Noel Pineda Lezama, Omar Bonerge Tafur Cabrera, Jorge Lucena León Castro, Bellanith Ruth Redondo Bilbao, Osman Pérez Coronel, Leidy |
| dc.subject.spa.fl_str_mv |
Intelligent data retrieval Data Warehouse Unique Identification Number Academic performance |
| topic |
Intelligent data retrieval Data Warehouse Unique Identification Number Academic performance |
| description |
In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students. |
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2019 |
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2019-08-08T14:36:31Z |
| dc.date.available.none.fl_str_mv |
2019-08-08T14:36:31Z |
| dc.date.issued.none.fl_str_mv |
2019-06-26 |
| dc.type.spa.fl_str_mv |
Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Text |
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978-3-030-22807-1 978-3-030-22808-8 |
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http://hdl.handle.net/11323/5132 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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https://doi.org/10.1007/978-3-030-22808-8_20 |
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1. Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017) Google Scholar 2. Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimiento 6(1), 1–17 (2009) Google Scholar 3. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web visibility profiles of top100 Latin American universities. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_24 Google Scholar 4. 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, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63 Google Scholar 5. Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística 9(1), 93–106 (2016) Google Scholar 6. Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Designing data warehouses: from business requirement analysis to multidimensional modeling. In Proceedings of the 1st International Workshop on Requirements Engineering for Business Need and IT Alignment, Paris, France (2005) Google Scholar 7. Vásquez, C., et al.: Cluster of the Latin American universities top100 according to webometrics 2017. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_26 Google Scholar 8. Haykin, S.: Neural Networks a Comprehensive Foundation, 2nd edn. Macmillan College Publishing Inc., USA (1999). ISBN 9780023527616 Google Scholar 9. Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London (2004). ISBN 8420540250 Google Scholar 10. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, New Jersey (2009) Google Scholar 11. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003) Google Scholar 12. Kuan, C.M.: Artificial neural networks. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2008) Google Scholar 13. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996) Google Scholar 14. Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014) Google Scholar 15. Sekmen, F., Kurkcu, M.: An early warning system for Turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Finan. Rev. 4(1), 529–543 (2014) Google Scholar 16. Singhal, D., Swarup, K.S.: Electricity price forecasting using artificial neural networks. IJEPE 33(1), 550–555 (2011) Google Scholar 17. Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015) Google Scholar 18. Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2009) Google Scholar 19. Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36318-4_3 Google Scholar 20. Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Ann. Oper. Res. 168(1), 151–168 (2009) Google Scholar 21. Abhay, K.A., Badal, N.A.: Novel approach for intelligent distribution of data warehouses. Egypt. Inf. J. 17(1), 147–159 (2015) Google Scholar 22. Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for data mining association rules in large databases. In: Proceedings of 21st Very Large Data Base Conference, vol. 5, no. 1, pp. 432–444 (1995) Google Scholar 23. Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W.: Java agents for meta learning over distributed databases. In: Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining, vol. 5, no. 2, pp. 74–81 (1997) Google Scholar 24. Prodromidis, A., Chan, P.K., Stolfo, S.J.: Meta learning in distributed data mining systems: issues and approaches. In: Kargupta, H., Chan, P. (eds.) Book on Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press, Cambridge (2000) Google Scholar 25. Parthasarathy, S., Zaki, M.J., Ogihara, M.: Parallel data mining for association rules on shared-memory systems. Knowl. Inf. Syst. Int. J. 3(1), 1–29 (2001) Google Scholar 26. Grossman, R.L., Bailey, S.M., Sivakumar, H., Turinsky, A.L.: Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proceedings of ACM/IEEE Conference on Supercomputing, vol. 63, pp. 1–14 (1999) Google Scholar 27. Chattratichat, J., Darlington, J., Guo, Y., Hedvall, S., Köhler, M., Syed, J.: An architecture for distributed enterprise data mining. In: Sloot, P., Bubak, M., Hoekstra, A., Hertzberger, B. (eds.) HPCN-Europe 1999. LNCS, vol. 1593, pp. 573–582. Springer, Heidelberg (1999). https://doi.org/10.1007/BFb0100618 Google Scholar 28. Wang, L., Tao, J., Ranjan, R., Marten, H., Streit, A., Chen, J., Chen, D.: G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Gener. Comput. Syst. 29(3), 739–750 (2013) Google Scholar 29. Butenhof, D.R.: Programming with POSIX Threads. Addison-Wesley Longman Publishing Company, Boston (1997) Google Scholar 30. Bhaduri, K., Wolf, R., Giannella, C., Kargupta, H.: Distributed decision-tree induction in peer-to-peer systems. Stat. Anal. Data Min. 1(2), 85–103 (2008) Google Scholar 31. Instituto colombiano para la Evaluación de la Educación - ICFES. Informe nacional de resultados Saber Pro 2015–2018. ICFES, Bogotá (2018) Google Scholar 32. Rafailidis, D., Kefalas, P., Manolopoulos, Y.: Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74(1), 11–18 (2017) Google Scholar 33. Zheng, C., Haihong, E., Song, M., Song, J.: CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205(1), 141–151 (2016) Google Scholar 34. Hidasi, B., Tikk, D.: Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 67–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_5 Google Scholar 35. Lee, J., Lee, D., Lee, Y.C., Hwang, W.S., Kim, S.W.: Improving the accuracy of top-n recommendation using a preference model. Inf. Sci. 348(1), 290–304 (2016) Google Scholar 36. Abhay, K.A., Neelendra, B.: Data storing in intelligent and distributed data warehouse using unique identification number. Int. J. Grid Distrib. Comput. 10(9), 13–32 (2017) Google Scholar |
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Silva, Jesúse17281d02925301aa71681ad0d7b3e03Hernández, Lissetted1006a6baa1332b29c8e41f18187e598Varela, Noel544417e3ea23421c46114ee4f01f436aPineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bdTafur Cabrera, Jorgeda6b8e453a7b44773b5aa44141b53523Lucena León Castro, Bellanith Ruth5441bbde431ced72853be6645bc4734fRedondo Bilbao, Osman29d9369bdd2973df07f904f03da95cacPérez Coronel, Leidy3426bcbd6de7732cf56d1a4e7d4af84f2019-08-08T14:36:31Z2019-08-08T14:36:31Z2019-06-26978-3-030-22807-1978-3-030-22808-8http://hdl.handle.net/11323/5132Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students.engInternational Symposium on Neural Networkshttps://doi.org/10.1007/978-3-030-22808-8_201. Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017) Google Scholar 2. Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimiento 6(1), 1–17 (2009) Google Scholar 3. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web visibility profiles of top100 Latin American universities. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_24 Google Scholar 4. 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, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63 Google Scholar 5. Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística 9(1), 93–106 (2016) Google Scholar 6. Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Designing data warehouses: from business requirement analysis to multidimensional modeling. In Proceedings of the 1st International Workshop on Requirements Engineering for Business Need and IT Alignment, Paris, France (2005) Google Scholar 7. Vásquez, C., et al.: Cluster of the Latin American universities top100 according to webometrics 2017. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_26 Google Scholar 8. Haykin, S.: Neural Networks a Comprehensive Foundation, 2nd edn. Macmillan College Publishing Inc., USA (1999). ISBN 9780023527616 Google Scholar 9. Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London (2004). ISBN 8420540250 Google Scholar 10. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, New Jersey (2009) Google Scholar 11. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003) Google Scholar 12. Kuan, C.M.: Artificial neural networks. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2008) Google Scholar 13. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996) Google Scholar 14. Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014) Google Scholar 15. Sekmen, F., Kurkcu, M.: An early warning system for Turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Finan. Rev. 4(1), 529–543 (2014) Google Scholar 16. Singhal, D., Swarup, K.S.: Electricity price forecasting using artificial neural networks. IJEPE 33(1), 550–555 (2011) Google Scholar 17. Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015) Google Scholar 18. Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2009) Google Scholar 19. Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36318-4_3 Google Scholar 20. Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Ann. Oper. Res. 168(1), 151–168 (2009) Google Scholar 21. Abhay, K.A., Badal, N.A.: Novel approach for intelligent distribution of data warehouses. Egypt. Inf. J. 17(1), 147–159 (2015) Google Scholar 22. Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for data mining association rules in large databases. In: Proceedings of 21st Very Large Data Base Conference, vol. 5, no. 1, pp. 432–444 (1995) Google Scholar 23. Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W.: Java agents for meta learning over distributed databases. In: Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining, vol. 5, no. 2, pp. 74–81 (1997) Google Scholar 24. Prodromidis, A., Chan, P.K., Stolfo, S.J.: Meta learning in distributed data mining systems: issues and approaches. In: Kargupta, H., Chan, P. (eds.) Book on Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press, Cambridge (2000) Google Scholar 25. Parthasarathy, S., Zaki, M.J., Ogihara, M.: Parallel data mining for association rules on shared-memory systems. Knowl. Inf. Syst. Int. J. 3(1), 1–29 (2001) Google Scholar 26. Grossman, R.L., Bailey, S.M., Sivakumar, H., Turinsky, A.L.: Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proceedings of ACM/IEEE Conference on Supercomputing, vol. 63, pp. 1–14 (1999) Google Scholar 27. Chattratichat, J., Darlington, J., Guo, Y., Hedvall, S., Köhler, M., Syed, J.: An architecture for distributed enterprise data mining. In: Sloot, P., Bubak, M., Hoekstra, A., Hertzberger, B. (eds.) HPCN-Europe 1999. LNCS, vol. 1593, pp. 573–582. Springer, Heidelberg (1999). https://doi.org/10.1007/BFb0100618 Google Scholar 28. Wang, L., Tao, J., Ranjan, R., Marten, H., Streit, A., Chen, J., Chen, D.: G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Gener. Comput. Syst. 29(3), 739–750 (2013) Google Scholar 29. Butenhof, D.R.: Programming with POSIX Threads. Addison-Wesley Longman Publishing Company, Boston (1997) Google Scholar 30. Bhaduri, K., Wolf, R., Giannella, C., Kargupta, H.: Distributed decision-tree induction in peer-to-peer systems. Stat. Anal. Data Min. 1(2), 85–103 (2008) Google Scholar 31. Instituto colombiano para la Evaluación de la Educación - ICFES. Informe nacional de resultados Saber Pro 2015–2018. ICFES, Bogotá (2018) Google Scholar 32. Rafailidis, D., Kefalas, P., Manolopoulos, Y.: Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74(1), 11–18 (2017) Google Scholar 33. Zheng, C., Haihong, E., Song, M., Song, J.: CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205(1), 141–151 (2016) Google Scholar 34. Hidasi, B., Tikk, D.: Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 67–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_5 Google Scholar 35. Lee, J., Lee, D., Lee, Y.C., Hwang, W.S., Kim, S.W.: Improving the accuracy of top-n recommendation using a preference model. Inf. Sci. 348(1), 290–304 (2016) Google Scholar 36. Abhay, K.A., Neelendra, B.: Data storing in intelligent and distributed data warehouse using unique identification number. Int. J. Grid Distrib. 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