ABSTRACT
Big Data refers to extremely large, diverse, and complex sets of data that cannot be easily managed, processed, or analyzed using traditional data processing methods. It encompasses both structured and unstructured data from various sources, including but not limited to sensors, social media, transactions, logs, and multimedia. Big Data plays a crucial role in the context of the Internet of Things (IoT). This chapter provides a comprehensive exploration of the crucial aspects of data management and analytics within the context of the IoT. The chapter begins with an introduction to Big Data, its architecture, and the associated challenges in the IoT domain. It further delves into the importance of data handling and analytics in IoT, explores its diverse applications, and examines different types of data analytics techniques. Next, the chapter delves into the challenges associated with handling Big Data in the context of IoT. It explores issues related to data acquisition, storage, transmission, and real-time processing, highlighting the unique complexities that arise due to the massive scale, heterogeneity, and velocity of data generated by IoT devices. Furthermore, the chapter emphasizes the critical role of data handling and analytics in the IoT ecosystem. It elucidates how effective data management and analysis can unlock the full potential of IoT-generated data, enabling organizations to derive actionable insights, make informed decisions, and optimize operations. The chapter explores various applications of data handling and analytics in IoT, ranging from predictive maintenance and anomaly detection to personalized healthcare and smart cities. Moreover, the chapter provides an overview of different types of data analytics techniques used in IoT. It covers descriptive analytics, which involves summarizing and visualizing data to gain a better understanding of past events. It also delves into diagnostic analytics, which focuses on identifying the causes and factors contributing to certain outcomes. Additionally, the chapter explores predictive analytics, which utilizes historical data to make informed predictions and forecasts. Finally, it discusses prescriptive analytics, which recommends optimal actions based on data analysis and simulation.
