Driving Performance Improvement of an Organization through Data Object Fusion
Article Sidebar
Main Article Content
Abstract
To succeed in today’s data-driven economy, organizations must find ways to put their massive data stores to work competitively. This research delves into the possibility of using data object fusion techniques and, more significantly, consensus clustering to boost the efficiency of businesses in an area of expertise. A case investigation of the automotive service sector demonstrates potential results and puts theoretical knowledge into practice within an organization. Therefore, this study addresses the prospective benefits of data object fusion in the automotive service sector. Furthermore, by combining the findings of different clustering methods, consensus clustering can provide a more precise and reliable outcome. Moreover, a consistent representation of the data objects is obtained by applying this technique to disparate datasets acquired from different sources inside the organization, which improves decision-making and productivity in operations. The research highlights the significance of data quality and the selection of proper clustering techniques to achieve dependable and accurate data object fusion. The findings add to the expanding knowledge of using data-driven ways to enhance organizational performance in any emerging sector.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
De Vin, L. J., Ng, A. H. C., Oscarsson, J., & Andler, S. F. (2006). Information fusion for simulation-based decision support in manufacturing. Robotics and Computer-Integrated Manufacturing, 22(5-6), 429–436.
Sanchez-Pi, N., Martí, L., Molina, J. M., & Bicharra García, A. C. (2016). Information Fusion for Improving Decision-Making in Big Data Applications. Computer Communications and Networks, 171–188.
Meng, F., Li, A., & Liu, Z. (2022). An Evidence theory and data fusion-based classification method for decision making. Procedia Computer Science, 199, 892–899.
Almalawi, A., Khan, A.I., Alsolami, F., Abushark, Y.B. and Alfakeeh, A.S., 2023. Managing Security of Healthcare Data for a Modern Healthcare System. Sensors, 23(7), p.3612.
Huang, C., & Huang, Y. (2022). Information fusion early warning of rail transit signal operation and maintenance based on big data of the Internet of things. Sustainable Computing: Informatics and Systems, 35, 100763.
Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2022). Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions. Mobile Networks and Applications.
Karthick Raghunath, K. M., Koti, M. S., Sivakami, R., Vinoth Kumar, V., NagaJyothi, G., & Muthukumaran, V. (2022). Utilization of IoT-assisted computational strategies in wireless sensor networks for smart infrastructure management. International Journal of System Assurance Engineering and Management.
Pandiyan, S., M., A., R., M., K.M., K. R., & G.R., A. R. (2020). Heterogeneous Internet of Things organization Predictive Analysis Platform for Apple Leaf Diseases Recognition. Computer Communications, 154, 99–110.
[Dumancas, G. G., Krichbaum, M., Solivio, B., Lubguban, A. A., & Malaluan, R. M. (2023). Data fusion applications in toxicology. Reference Module in Biomedical Sciences.
H. Almulihi, A., Alassery, F., Irshad Khan, A., Shukla, S., Kumar Gupta, B., & Kumar, R. (2022). Analyzing the Implications of Healthcare Data Breaches through Computational Technique. Intelligent Automation & Soft Computing, 32(3), 1763–1779.
Zheng, D., & Wang, Q. (2013). Selection algorithm for K-means initial clustering center. Journal of Computer Applications, 32(8), 2186–2188.
J. Ross Quinlan. (1993). Combining Instance-Based and Model-Based Learning. Elsevier EBooks, 236–243.
Kanghua Hui, & Chunheng Wang. (2008). Clustering-based locally linear embedding. 2008 19th International Conference on Pattern Recognition.
Afghani, S. A., & Putra, W. Y. M. (2021). Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy. Indonesian Journal of Statistics and Its Applications, 5(2), 369–376.
Jun Wang. (1999). A linear assignment clustering algorithm based on the least similar cluster representatives. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 29(1), 100–104.
Kumar, R., Khan, A.I., Abushark, Y.B., Alam, M.M., Agrawal, A. and Khan, R.A., 2020. An integrated approach of fuzzy logic, AHP and TOPSIS for estimating usable-security of web applications. IEEE Access, 8, pp.50944-50957.
sklearn. cluster. Spectral Clustering. (n.d.). Scikit-Learn. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html
Ling, G., Wang, M., & Feng, J. (2011). Clustering ensemble method based on co-occurrence similarity. Journal of Computer Applications, 31(2), 441–445.
Data Set - OpenXC. (n.d.). Openxcplatform.com. Retrieved May 12, 2023, from http://openxcplatform.com/about/data-set.html
von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416.
Alam, M.M., Khan, A.I. and Zafar, A., 2016. A comprehensive study of software product line frameworks. International Journal of Computer Applications, 151(3).
Barnawi, A., Al-Talhi, A.H., Qureshi, M. and Khan, A.I., 2012. Novel component based development model for sip-based mobile application. arXiv preprint arXiv:1202.2516.
Survey Report on K-Means Clustering Algorithm. (2017). International Journal of Modern Trends in Engineering & Research, 4(4), 218–221.
Almalawi, A., Khan, A.I., Alsolami, F., Abushark, Y.B. and Alfakeeh, A.S., 2023. Managing Security of Healthcare Data for a Modern Healthcare System. Sensors, 23(7), p.3612.