부산대학교 도서관

The exponential growth of large, complex, and dynamic datasets, known as big data, required the development of robust analytical techniques beyond conventional tools. This article examines the landscape of big data processing techniques like LSTM, CNN, and more identifying their suitability for different application domains like social media, finance, healthcare, and agriculture. Recognizing the frequent time-sensitive nature of big data analysis, we emphasize the importance of data quality considerations within the processing pipeline. Through a structured analysis of existing literature, we present a comprehensive overview of diverse big data analytical techniques, challenges, technologies, and applications, including statistical methods, machine learning algorithms, and distributed computing frameworks. Our key findings reveal the strengths and limitations of each approach, highlighting the need for hybrid solutions and domain-specific adaptations. Furthermore, we discuss the broader implications of big data processing on knowledge extraction, decision-making, and ethical considerations for responsible data analysis in various fields.