Noise Filtering for Big Data Analytics

1. Auflage 2021
ISBN: 978-3-11-069709-4
Verlag: De Gruyter

Buch, Englisch, 156 Seiten, Format (B × H): 240 mm x 170 mm, Gewicht: 543 g

Reihe: De Gruyter series on the applications of mathematics in engineering and information sciences

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.

Zielgruppe

researchers, students and experienced professionals

Autoren/Hrsg.

Bhattacharyya, Souvik

Ghosh, Koushik

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Über Autor(innen)

Souvik Bhattacharyya, Koushik Ghosh, University of Burdwan,West Bengal, India.