Design & Develop: Data Warehouse & Data Mart for Business Organization

Authors

  • Farhad Khoshbakht, Atena Shiranzaei, S. M. K. Quadri

Keywords:

DW, DM, BI, Top-down analysis, Bottom-up analysis and Integration

Abstract

Data warehouses (DW) are the foundation of business intelligence (BI) data storage. Business organizations utilize DW to help decision-making processes as large and complicated data sets must be examined and analysed. The analytical processing technology presupposes that data are presented as straightforward DM (data marts) with a well-defined set of facts and data analysis dimensions (star schema). Despite the widespread adoption of data warehouse technology and concepts, it becomes complex for the designers to identify and extract DM from an information system. This study strategy uses three fundamental steps for the methodology: top-down analysis, bottom-up analysis and integration. Though the method is not fully mechanical, it offers more direction than earlier methods to DW and DM designers.

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Author Biography

Farhad Khoshbakht, Atena Shiranzaei, S. M. K. Quadri

Farhad Khoshbakht1, Atena Shiranzaei2, S. M. K. Quadri3

1Department of Computer Engineering,Faculty of Industry and Mining(Khash),University of Sistan and Baluchestan,Zahedan,Iran 2ashiranzaei@eng.usb.ac.ir

2Department of Computer Science Jamia Millia Islamia (A Central University), New Delhi, India 110025

1f.khoshbakht630@gmail.com

3Department of Computer Science Jamia Millia Islamia(A Central University), New Delhi, India 110025

3quadrismk@jmi.ac.in

References

Adela, B., & Diaconita, V. (2009). A model for Business Intelligence Systems’ Development Adaptive Information Systems View project Intelligent system for trading on wholesale electricity market (SMARTRADE) View project. https://www.researchgate.net/publication/40646563

Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766. https://doi.org/10.1016/j.techfore.2021.120766

Božič, K., & Dimovski, V. (2019). Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective. The Journal of Strategic Information Systems, 28(4), 101578. https://doi.org/10.1016/j.jsis.2019.101578

Brannigan, C., Harling, M., & Townend, M. (2016). The factors that influence decision making by 8-12 year olds in Child and Adolescent Mental Health Services (CAMHS): a systematic review. https://www.researchgate.net/publication/305787345

Faisal, S., Sarwar, M., Shahzad, K., Sarwar, S., Jaffry, W., & Yousaf, M. M. (2017). Temporal and Evolving Data Warehouse Design. Scientific Programming, 2017, 1–18. https://doi.org/10.1155/2017/7392349

Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38–56. https://doi.org/10.1016/j.im.2016.03.009

Graydon, C., Teede, H., Sullivan, C., de Silva, K., & Enticott, J. (2022). Driving impact through big data utilization and analytics in the context of a Learning Health System. In Big Data Analytics for Healthcare (pp. 13–22). Elsevier. https://doi.org/10.1016/B978-0-323-91907-4.00019-4

Gudfinnsson, K., Strand, M., & Berndtsson, M. (2015). Analyzing business intelligence maturity. Journal of Decision Systems, 24(1), 37–54. https://doi.org/10.1080/12460125.2015.994287

He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G., & Tao, R. (2015). Gaining competitive intelligence from social media data. Industrial Management & Data Systems, 115(9), 1622–1636. https://doi.org/10.1108/IMDS-03-2015-0098

Hurtado, C. A., & Gutierrez, C. (2007). Handling Structural Heterogeneity in OLAP. In Data Warehouses and OLAP (pp. 27–57). IGI Global. https://doi.org/10.4018/987-1-59904-364-7.ch002

Lechtenbörger, J. (2001). Data Warehouse Schema Design *.

Moody, D., Kortink, M., Moody, D. L., & Kortink, M. A. R. (2010). From Enterprise Models to Dimensional Models: A Methodology for Data Warehouse and Data Mart Design. http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-28/

Paradza, D., & Daramola, O. (2021). Business Intelligence and Business Value in Organisations: A Systematic Literature Review. Sustainability, 13(20), 11382. https://doi.org/10.3390/su132011382

Ramesh, G. S., Rajini Kanth, T. v., & Vasumathi, D. (2020a). A Comparative Study of Data Mining Tools and Techniques for Business Intelligence (pp. 163–173). https://doi.org/10.1007/978-981-13-8253-6_15

Ramesh, G. S., Rajini Kanth, T. v., & Vasumathi, D. (2020b). A Comparative Study of Data Mining Tools and Techniques for Business Intelligence (pp. 163–173). https://doi.org/10.1007/978-981-13-8253-6_15

Rouhani, S., Ashrafi, A., Zare Ravasan, A., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29(1), 19–50. https://doi.org/10.1108/JEIM-12-2014-0126

Shanks, G., & Bekmamedova, N. (2012a). Achieving benefits with business analytics systems: an evolutionary process perspective. Journal of Decision Systems, 21(3), 231–244. https://doi.org/10.1080/12460125.2012.729182

Shanks, G., & Bekmamedova, N. (2012b). Achieving benefits with business analytics systems: an evolutionary process perspective. Journal of Decision Systems, 21(3), 231–244. https://doi.org/10.1080/12460125.2012.729182

Sharda, R., Delen, D., & Turban, E. (2015). Business Intelligence and Analytics: Systems for Decision Support, 10th Edition.

Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). Towards felicitous decision making: An overview on challenges and trends of Big Data. Information Sciences, 367–368, 747–765. https://doi.org/10.1016/j.ins.2016.07.007

Wieder, B., Ossimitz, M. L., & Chamoni, P. (2013). The Impact of Business Intelligence Tools on Performance: A User Satisfaction Paradox? Business Intelligence Research at UTS (Accounting).

Williams, S., & William. (2007). The Profit Impact of Business Intelligence. Elsevier. https://doi.org/10.1016/B978-0-12-372499-1.X5000-5

BIFramework(Sharda et al., 2015)

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Published

04.02.2023

How to Cite

Farhad Khoshbakht, Atena Shiranzaei, S. M. K. Quadri. (2023). Design & Develop: Data Warehouse & Data Mart for Business Organization. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 260–265. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2682

Issue

Vol. 11 No. 3s (2023)

Section

Research Article

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Copyright (c) 2023 Farhad Khoshbakht, Atena Shiranzaei, S. M. K. Quadri

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