Abstract

In the era of big data, high-dimensional datasets generated by IOT systems pose significant challenges for data analysis and decision-making. This book chapter provides a comprehensive exploration of advanced techniques for handling high-dimensional IoT data, with a specific focus on scalable methodologies that address the inherent complexities of non-linear relationships, sparsity, and missing values. The chapter delves into cutting-edge approaches for real-time feature extraction, including incremental and distributed dimensionality reduction methods, and evaluates the integration of cloud-based services for managing large-scale data. Emphasis was placed on non-linear autoencoders, which offer powerful capabilities for compressing complex datasets, and strategies for handling missing values during dimensionality reduction, ensuring robust and reliable data analysis. Key applications and future research directions are discussed to highlight the evolving landscape of high-dimensional data processing. This chapter serves as an essential resource for researchers and practitioners seeking to advance their understanding and application of dimensionality reduction techniques in high-dimensional IoT contexts.

Introduction

In the contemporary landscape of big data, high-dimensional datasets generated by IOT systems present complex challenges that demand sophisticated analytical techniques [1]. The proliferation of IoT devices has led to an unprecedented volume of data characterized by an immense number of features and variables [2]. This high dimensionality poses significant difficulties for traditional data processing methods, as the curse of dimensionality exacerbates issues related to computational efficiency, storage requirements, and data interpretation [3]. As datasets grow in size and complexity, conventional approaches often struggle to maintain performance and accuracy, highlighting the need for advanced dimensionality reduction techniques that can effectively manage and analyze high-dimensional data [4,5].

To address the challenges posed by high-dimensional IoT data, scalable dimensionality reduction techniques have emerged as crucial tools [6,7]. These techniques aim to reduce the number of features while preserving the essential characteristics and patterns of the data [8]. Real-time feature extraction methods, such as incremental and distributed dimensionality reduction, play a vital role in managing dynamic and streaming data [9]. By employing algorithms that update and adapt to new information in real-time, these methods ensure that data processing remains efficient and timely [10]. Additionally, cloud-based services offer scalable solutions that leverage distributed computing resources, enabling the handling of large-scale datasets with enhanced efficiency and reduced computational overhead [11].