Data Analytics and Engineering in Automobile Data Systems - Journal of Science & Technology
Keywords:
data analytics, engineeringAbstract
Data analytics and engineering are transforming vehicle design, production, and customer experience in the auto industry. As the sector faces enormous problems including sustainability, changing customer needs, and disruptive technology, data-driven methods are key to innovation. This study examines how data analytics and engineering improve production processes, product quality, and predictive maintenance in the automotive industry.
Analytical applications in market segmentation, tailored marketing, and post-sales services are central to this research. Automakers are adapting solutions to individual demands and enhancing user experience by researching consumer preferences and driving patterns. Data analytics-powered connected car networks and OTA updates allow manufacturers to continuously enhance vehicle software, improving functionality and customer happiness.
References
1. Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
2. Sivaraman, Hariprasad. "Intelligent Code Coverage Optimization Using Machine Learning for Large Scale Systems." International Journal for Multidisciplinary Research 5.5 (2023).
3. Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
4. S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
5. Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
6. Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
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