Noise Filtering For Big Data Analytics
Download Noise Filtering For Big Data Analytics PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Noise Filtering For Big Data Analytics book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Noise Filtering for Big Data Analytics
Author: Souvik Bhattacharyya
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2022-06-21
This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Big Data Preprocessing
This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.
Big Data Analytics Methods
Author: Peter Ghavami
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2019-12-16
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.