Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Guitart, I., & Conesa, J. (2015). Analytic information systems in the context of higher education: Expectations, reality and trends. In 2015 international conference on intelligent networking and collaborative systems (pp. 294–300). Taipei.
Zorrilla, M.-E., Marin,D., & Alvarez, E. (2007). Towards virtual course evaluation using web intelligence. In EUROCAST 2007 (pp. 392–399). Heidelberg: Springer.
Falakmasir, M.-H., Moaven, S., Abolhassani, H., & Habibi, J. (2010). Business intelligence in e-learning: (Case study on the Iran university of science and technology dataset). In The 2nd international conference on software engineering and data mining (pp. 473–477). Chengdu.
Rodzi, N.-A.-H.-M., Othman, M.-S., & Yusuf, L.-M. (2015). Significance of data integration and ETL in business intelligence framework for higher education. In International conference on science in information technology (ICSITech) (pp. 181–186). Yogyakarta.
Gounder, M.-S., Iyer, V.-V., & Mazyad, A.-A. (2016). A survey on business intelligence tools for university dashboard development. In 3rd MEC international conference on big data and smart city (ICBDSC) (pp. 1–7). Muscat.
Devasia, T., Vinushree, T.-P., & Hegde, V. (2016). Prediction of students performance using educational data mining. In International conference on data mining and advanced computing (SAPIENCE) (pp. 91–95). Ernakulam.
Jayakody, J., & Perera, I. (2016). Enhancing competencies of less-able students to achieve learning outcomes: Learner aware tool support through business intelligence. In IEEE international conference on teaching, assessment, and learning for engineering (TALE) (pp. 154–160). Bangkok.
SQL Server Data Tools. https://docs.microsoft.com/en-us/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-2017. Accessed 1 Feb 2018.
Leonard, A., Masson, M., Mitchell, T., Moss. J.-M., & Ufford, M. (2012). Data cleansing with data quality services. In SQL server 2012 integration services design patterns (pp. 101–122). Berkeley: Apress. CrossRef
Leonard, A., Masson, M., Mitchell, T., Moss, J.-M., & Ufford, M. (2014). Data correction with data quality services. In SQL server 2012 integration services design patterns (pp. 101–123). Berkeley: Apress.
Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. Hoboken: Wiley.
Singh, R.-P., & Singh, K. (2016). Design and research of data analysis system for student education improvement (case study: Student progression system in university). In International conference on micro-electronics and telecommunication engineering (ICMETE) (pp. 508–512). Ghaziabad.
James, J.-A. (2015). SQL server analysis services an hour a day. SC, USA: CreateSpace Independent Publishing Platform.
Boulila, W., Farah, I.-R., & Hussain, A. (2018). A novel decision support system for the interpretation of remote sensing big data. Earth Science Informatics, 11(1), 31–45. CrossRef
Taibah University. https://www.taibahu.edu.sa/Pages/en/CustomPage.aspx?ID=47. Accessed 20 May 2018.
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017). Group sparse regularization for deep neural networks. Neurocomputing, 241, 81–89. CrossRef
Chebbi, I., Boulila, W., & Farah, I.-R. (2016). Improvement of satellite image classification: Approach based on Hadoop/MapReduce. Advanced technologies for signal and image processing (pp. 31–34).
Chebbi, I., Boulila, W., & Farah, I.-R. (2015). Big data: Concepts, challenges and applications. In International conference on computational collective intelligence (pp. 638–647). CrossRef
Boulila, W., Farah, I.-R., Saheb Ettabaa, K., Solaiman, B., & Ben Ghezala, H. (2009). Improving spatiotemporal change detection: A high level fusion approach for discovering uncertain knowledge from satellite image databases. In: International conference on data mining (vol. 58, pp. 222–227). Italy.
Ferchichi, A., Boulila, W., & Farah, I.-R. (2017). Towards an uncertainty reduction framework for land-cover change prediction using possibility theory. Vietnam Journal of Computer Science, 4(3), 195–209. CrossRef
Ferchichi, A., Boulila, W., & Farah, I.-R. (2017). Propagating aleatory and epistemic uncertainty in land cover change prediction process. Ecological Informatics, 37, 24–37. CrossRef
Boulila, W., Ayadi, Z., & Farah, I.-R. (2017). Sensitivity analysis approach to model epistemic and aleatory imperfection: Application to land cover change prediction model. Journal of Computational Science, 23, 58–70. CrossRef
- A business intelligence based solution to support academic affairs: case of Taibah University
- Springer US
The Journal of Mobile Communication, Computation and Information
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
Neuer Inhalt/© Filograph | Getty Images | iStock