Noise Filtering for Big Data Analytics

This book provides methods for performing data de-noising in large scale with accuracy, addressing error propagation, maintaining data positional importance, and preserving memory. It is essential for analyzing noisy big data to avoid misleading conclusions.

Format: Hardback
Length: 164 pages
Publication date: 21 June 2022
Publisher: De Gruyter

This comprehensive book delves into the intricate process of data de-noising, catering to large-scale applications with remarkable accuracy. It addresses three fundamental challenges: mitigating error propagation during the development of filtered models, preserving the positional significance of data during purification, and ensuring the retention of memory in noisy big data.

If, after applying smoothing or filtering techniques, the memory of the corresponding data undergoes significant alterations, the final data may suffer the loss of crucial information. This can result in incorrect or misleading conclusions. However, anticipating information loss due to smoothing or filtering is inevitable when analyzing big data in the presence of noise. Consequently, the entire denoising process necessitates meticulous execution with efficient and intelligent models to effectively manage it.

The book explores various techniques and algorithms for data de-noising, including linear and non-linear filtering, wavelet transforms, and machine learning approaches. It emphasizes the importance of selecting appropriate models based on the characteristics of the data and the desired level of accuracy. The authors also discuss the significance of pre-processing steps, such as data normalization and feature extraction, in enhancing the performance of data de-noising algorithms.

Furthermore, the book explores the application of data de-noising in various fields, such as image processing, signal processing, and medical imaging. It discusses the challenges associated with handling large datasets and the importance of parallel processing and distributed computing techniques to efficiently handle massive amounts of data.

In conclusion, this book serves as a valuable resource for researchers, practitioners, and students interested in data de-noising and its applications. It provides a comprehensive understanding of the techniques, algorithms, and challenges involved in data de-noising, enabling individuals to effectively handle noisy big data and extract valuable insights from it.

Weight: 543g
Dimension: 240 x 170 (mm)
ISBN-13: 9783110697094