Optimizing Business Intelligence System Using Big Data and Machine Learning
Abstract
The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to make the best decisions for the situation at any given time. The relentless collection of data from user interactions have introduced both a high level of complexity, as well as a great opportunity for businesses. In addition to connecting not just people, but also machines to the internet, and then collecting data from these machines via sensors would result in an unimaginable repository of data. This ever-increasing collection of data is known as Big Data. Integrating this with existing Business intelligence systems and deep analysis using Machine Learning algorithms, Big Data can give useful insights into business problems and perhaps even to make suggestions as to when and where future problems will occur (Predictive Analysis) so that problems can be avoided or at least mitigated. This paper targets at developing a system capable of optimizing a business intelligence using big data and machine learning approach. The design of a system to optimize the Business Intelligence System using Machine Learning and Big Data at NNPC was successfully carried out. The System was able to automatically analyze the sample report under NNPC permission to use and it generated expected predictive outputs which serves as a better guide to managers. When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures.
Downloads
References
B. S. Sahay and J. Ranjan, “Real time business intelligence in supply chain analytics,” Inf. Manag. Comput. Secur., vol. 16, no. 1, pp. 28–48, Mar. 2008, doi: 10.1108/09685220810862733.
G. G. James, E. G. Chukwu, and P. O. Ekwe, “Design of an Intelligent based System for the Diagnosis of Lung Cancer,” Int. J. Innov. Sci. Res. Technol., vol. 8, no. 6, pp. 791–796, 2023.
C. Ituma, G. G. James, and F. U. Onu, “A Neuro-Fuzzy Based Document Tracking & Classification System,” Int. J. Eng. Appl. Sci. Technol., vol. 4, no. 10, pp. 414–423, Feb. 2020, doi: 10.33564/IJEAST.2020.v04i10.075.
N. Okwuchukwu, D. Eseme, and A. Charlyn, “Commercialisation Of Public Enterprises In Nigeria: A Study Of The Nigerian National Petroleum Corporation (Nnpc),” 2023.
S. Biswas and J. Sen, “A Proposed Architecture for Big Data Driven Supply Chain Analytics”.
S. LaValle, Lesser, E., Shockley, R., Hopkins, M. S., and Kruschsitz, N., “Big data, analytics and the path from insights to value,” MIT Sloan Manag. Rev., pp. 21–32, 2011.
H. Chen, R. H. L. Chiang, and V. C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact”.
Fitzgerald, Michael, “Training the Next Generation of Business Analytics Professionals,” vol. 56, no. 2, p. 1, Winter 2015.
Wills, Mary J., “Decisions Through Data: Analytics in Healthcare,” Journal of Healthcare Management, vol. 59, no. 4, pp. 254–262, Aug. 2014.
K. Saeed, A. Sidorova, and A. Vasanthan, “The Bundling of Business Intelligence and Analytics,” J. Comput. Inf. Syst., vol. 63, no. 4, pp. 781–792, Jul. 2023, doi: 10.1080/08874417.2022.2103856.
L. Duan and L. D. Xu, “Business Intelligence for Enterprise Systems: A Survey,” IEEE Trans. Ind. Inform., vol. 8, no. 3, pp. 679–687, Aug. 2012, doi: 10.1109/TII.2012.2188804.
G. George, M. R. Haas, and A. Pentland, “Big Data and Management,” Acad. Manage. J., vol. 57, no. 2, pp. 321–326, Apr. 2014, doi: 10.5465/amj.2014.4002.
X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, “Data Mining with Big Data”.
H.-T. Chang, N. Mishra, and C.-C. Lin, “IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis,” PLOS ONE, vol. 10, no. 11, p. e0141980, Nov. 2015, doi: 10.1371/journal.pone.0141980.
Amir Atiya, “Learning Algorithms for Neural Networks,” Retrieved from California Institute of Technology Pasadena database, 1991.
Asemi, A.; Baba, M.S.; Haji Abdullah, R.; Idris, N., “Fuzzy multi criteria decision making applications:,” in Proceedings of the 3rd International Conference on Computer Engineering & Mathematical Sciences (ICCEMS, Langkawi, Malaysia, 2014.
Y. Song, A. G. Schwing, R. S. Zemel, and R. Urtasun, “Training Deep Neural Networks via Direct Loss Minimization”.
D. A. Saldana, L. Starck, P. Mougin, B. Rousseau, and B. Creton, “Prediction of Flash Points for Fuel Mixtures Using Machine Learning and a Novel Equation,” Energy Fuels, vol. 27, no. 7, pp. 3811–3820, Jul. 2013, doi: 10.1021/ef4005362.
J. Sung, J. Lee, I.-M. Chung, and J.-H. Heo, “Hourly Water Level Forecasting at Tributary Affected by Main River Condition,” Water, vol. 9, no. 9, p. 644, Aug. 2017, doi: 10.3390/w9090644.
J. Wang, X. Wu, and C. Zhang, “Support vector machines based on K-means clustering for real-time business intelligence systems,” Int. J. Bus. Intell. Data Min., vol. 1, no. 1, p. 54, 2005, doi: 10.1504/IJBIDM.2005.007318.
Dan Pelleg; Andrew Moore, “X-means: Extending K-means with efficient estimation of the number of clusters,” in Proceedings of the 17th International Conference on Machine Learning, School of Computer Science, Carnegie Mellon University, Pitsburgh, PA 15213, USA, 2000, pp. 727–734.
Onu F. U.; Osisikankwu P. U.; Madubuike C. E.; James G. G., “Impacts of Object Oriented Programming on Web Application Development,” Int. J. Comput. Appl. Technol. Res., vol. 4, no. 9, pp. 706–710, 2015.
G. Gregory and O. A. Ejaita, “The International Journal of Science & Technoledge,” vol. 4, no. 7.
M. Kubat, An Introduction to Machine Learning. Cham: Springer International Publishing, 2017. doi: 10.1007/978-3-319-63913-0.
Raudel Ravelo Suárez, “Extensión Del Almacén De Datos Empresarial En Cimex,” 2015, doi: 10.13140/RG.2.1.3533.6089.
R. S, “System Analysis and Design,” J. Inf. Technol. Softw. Eng., vol. 02, no. 05, 2012, doi: 10.4172/2165-7866.S8-e001.
Honeywell, “Selecting the Right Technology for Tank Level Gauging for Custody Transfer Applications”, 2027.
H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, “Exploring Strategies for Training Deep Neural Networks”, Journal of machine learning research, vol. 10, no. 1, 2009.
Abstract views: 395 times
Download PDF: 252 times
Copyright (c) 2024 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
- I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
- I certify that all authors have approved the publication of this and there is no conflict of interest.
- I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
- I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
- I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
- If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
- I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)
_1.png){kind=logo}
