Noise Filtering for Big Data Analytics
Language: English
Published by De Gruyter, 2022
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 141.42
Quantity: 2 available
Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9783110697094
- Title
- Noise Filtering for Big Data Analytics
- Author
- Souvik Bhattacharyya
- Publisher
- De Gruyter
- Publication year
- 2022
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3110697092
- ISBN 13
- 9783110697094
- Item weight
- 445 grams
- Dimensions
- 240x170x13 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Souvik Bhattacharyya, Koushik Ghosh, University of Burdwan,West Bengal, India.
"About the title" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 30 to 40 business days | 7 to 14 business days |
|---|---|---|
| First item | £ 53.42 | £ 62.00 |
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