Challenges and opportunities in big data analytics: Such as the risks and pitfalls of ignoring context/contextualization

• Main Author: Tan, David Chow Meng
• Format: Final Year Project / Dissertation / Thesis
• Published: 2024
• Subjects: H Social Sciences (General); HC Economic History and Conditions; T Technology (General)
• Online Access: http://eprints.utar.edu.my/6607/1/fyp_IA_2024_TDCM.pdf; http://eprints.utar.edu.my/6607/

This study explores the role of contextualization in big data analytics, emphasizing its significance across various applications including healthcare, urban planning, and network orchestration. The research introduces a novel context-aware recommender system designed to enhance user experience by integrating real-time contextual information seamlessly. Through extensive experiments using a Kaggle dataset, the study validates the system’s effectiveness in improving decision-making and operational efficiency. Methodologically, the project employs a comprehensive approach comprising data collection, preprocessing, exploration, and visualization, couple with advance d feature engineering and model evaluation. The findings demonstrates that contextualization significantly increases the precision and relevance of data analysis, there by fostering more informed decision-making. This research not only contributes to the academic discourse on big data but also offers practical insights for organizations aiming to leverage contextual data for strategic advantage.