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/
• Institution: Universiti Tunku Abdul Rahman

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.