Building Datawarehouse for Educational Institutions in 9 Steps

Rahutomo, Reza; Putri, Raissa Amanda; Pardamean, Bens

doi:10.1109/inapr.2018.8627010

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…In terms of decision-making research of higher education leading institutions, Jalota and Agrawal (2019) introduced the classification of educational data mining. On this basis, Rahutomo et al (2018) explored various possibilities and impacts of big data utilization in educational institutions to provide a scientific basis for the decision-making of higher education leading institutions. Dong (2023) built a higher education quality monitoring system based on big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Zhou,

2024

International Journal of E-Collaboration

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Integrated learning has attracted much attention from industry and academia. In the new era, colleges and universities need to discuss information management in light of actual conditions, integrate different data in each information system into the same database, so as to form a data warehouse based on the integrated database which can truly reflect the historical changes of data and provides support for managers' decision-making. This paper analyzes the clustering effect of standard differential evolution algorithm, improved differential evolution algorithm and K-means algorithm. The algorithm is tested using Iris and Wine database marts, the results show that the K-means algorithm is a relatively poor algorithm and its accuracy is significantly lower than the other two. Based on big data, multi-factor interactive variance analysis technology is used to analyze different data indicators and influencing factors. Therefore, colleges and universities can use the database to better understand the problems and advantages in management, thus to improve management efficiency and teaching level.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Zhou,

2024

International Journal of E-Collaboration

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“…En el mismo año, para generar estadísticas de información académica se propone una bodega de datos [12], con dos TH relacionadas con desempeño académico, una de ellas con cuatro dimensiones, Promedio: cohorte, período, semestre, género y medida promedio semestre. Y otra TH con tres dimensiones, Nota examen: cohorte, curso, clase y medida nota promedio curso.…”

Section: Estudio Y Análisis De Los Artículos Relevantesunclassified

“…Por otro lado, la creación de una solución de bodegas de datos por parte del personal interno de la institución resulta compleja ya que en la mayoría de los casos no están capacitados en el diseño e implementación de este tipo de soluciones.Uno de los aspectos de mayor dificultad al abordar este tipo de soluciones es la creación del modelo dimensional, esto debido a que su enfoque es diferente al de un modelo relacional, contemplando elementos y relaciones particulares. En la literatura se encuentran trabajos relacionados con bodegas de datos para matrícula académica y registro de notas[4,5,6,7,8,9,10,11,12,13,14]. Sin embargo, la mayoría de estos modelos presentan pocas dimensiones y/o atributos, y poca información socio-demográfica del estudiante, lo cual limita la capacidad de análisis sobre los mismos.…”

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Bodega de datos con alta capacidad de análisis para el desempeño académico de Universidades

Pito

Moreno

Mendoza

2020

Investigación e Innovación en Ingenierías

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Objetivo: Proponer una bodega de datos con alta capacidad de análisis para el desempeño de estudiantes universitarios; analizando las diferentes situaciones de diseño que pueden ser comunes para instituciones de educación superior, permitiendo obtener modelos más efectivos y facilitando la adaptación de estos. Metodología o método: Se identificaron dimensiones y medidas comunes encontradas en la literatura sobre bodegas de datos relacionados con el desempeño académico de los estudiantes, luego se incluyeron elementos propios de las universidades del país. Resultados: Dos modelos dimensionales para el desempeño académico de estudiantes en instituciones de educación superior y las situaciones comunes analizadas en el proceso de diseño de estos. Además, un prototipo de bodega de datos de estos modelos para la Universidad del Cauca y una evaluación basada en la norma ISO/IEC 25022, del Nivel de satisfacción de los usuarios con los modelos implementados. Conclusiones: Los resultados de la evaluación mostraron que el 87.72% de los usuarios quedaron satisfechos con los datos brindados por el modelo a través del prototipo, observando la importancia de este tipo de sistemas en las universidades.

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“…Implementation of the data warehouse in the television editorial is the one first done and still needs to be further developed. Researchers built this data warehouse using the Nine-Steps method Kimball in the analysis and design stages [18], then the application implementation using SQL Server [19], and Tableau [20]. Next using the Markov Model in the analysis and prediction of future trends.…”

Section: Introductionmentioning

confidence: 99%

Implementation of editorial television data warehouse: Health and business news will keep high since pandemic Covid-19

Suni¹,

Devianto²

2021

World J. Adv. Eng. Tech. Sci.

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Data warehouse technology in the world of informatics can be applied in television editors to predict events in the future. The results of implementing a data warehouse in television editors since the Covid-19 pandemic shows that in the future, television coverage will still be dominated by health news 80.64 percent and economic news 65.61 percent. In Indonesia, legal and criminal news reports have also become a news trend as a result of these health and economic problems. The television editorial data warehouse was designed using Kimball's Nine-Step method and implementation using Microsoft SQL Server and Tableau. And to predict the news in the future analyzed using the Markov model. Apart from analyzing television news, this data warehouse is also capable know the performance of the television editorial team, namely the performance of news producers, reporters, and cameramen. The results of this data warehouse can be used by television management and chief editors to make informed strategic decisions.

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Building Datawarehouse for Educational Institutions in 9 Steps

Cited by 8 publications

References 11 publications

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Bodega de datos con alta capacidad de análisis para el desempeño académico de Universidades

Implementation of editorial television data warehouse: Health and business news will keep high since pandemic Covid-19

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