Noise Filtering for Big Data Analytics / / ed. by Souvik Bhattacharyya, Koushik Ghosh.
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 import...
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Noise Filtering for Big Data Analytics / ed. by Souvik Bhattacharyya, Koushik Ghosh. Berlin ; Boston : De Gruyter, [2022] ©2022 1 online resource (VIII, 156 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , 2626-5427 ; 12 Frontmatter -- Preface -- Contents -- About the Editors -- Application of discrete domain wavelet filter for signal denoising -- Secret sharing scheme in defense and big data analytics -- Recent advances in digital image smoothing: A review -- Double exponential smoothing and its tuning parameters: A re-exploration -- Effect of smoothing on big data governed by polynomial memory -- Heteroskedasticity in panel data: A big challenge to data filtering -- Importance and use of digital filters in digital image processing -- Smart filter and smoothing: A new approach of data denoising -- Acknowledgement -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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. Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Mai 2023) Angewandte Mathematik. Big Data. Künstliche Intelligenz. Maschinelles Lernen. COMPUTERS / Information Technology. bisacsh Acharjee, Santanu, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bhattacharyya, Souvik, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bhattacharyya, Souvik, editor. edt http://id.loc.gov/vocabulary/relators/edt Chaudhuri, Dipta, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Dawud Adebayo, Agunbiade, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ghosh, Koushik, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ghosh, Koushik, editor. edt http://id.loc.gov/vocabulary/relators/edt Indu, Pabak, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Khan, Samarpita, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Khondekar, Mofazzal H., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mukherjee, Moloy, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Nureni Olawale, Adeboye, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Paul, Rimi, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Purkait, Souvik, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Saha, Gokul, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Samadder, Swetadri, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Sengupta, Anindita, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Sharma, Vivek, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Singh, Vijai, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1 9783110766820 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2022 English 9783110993899 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2022 9783110994810 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2022 English 9783110994223 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2022 9783110994193 ZDB-23-DEI EPUB 9783110697261 print 9783110697094 https://doi.org/10.1515/9783110697216 https://www.degruyter.com/isbn/9783110697216 Cover https://www.degruyter.com/document/cover/isbn/9783110697216/original |
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Acharjee, Santanu, Acharjee, Santanu, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Chaudhuri, Dipta, Chaudhuri, Dipta, Dawud Adebayo, Agunbiade, Dawud Adebayo, Agunbiade, Ghosh, Koushik, Ghosh, Koushik, Ghosh, Koushik, Ghosh, Koushik, Indu, Pabak, Indu, Pabak, Khan, Samarpita, Khan, Samarpita, Khondekar, Mofazzal H., Khondekar, Mofazzal H., Mukherjee, Moloy, Mukherjee, Moloy, Nureni Olawale, Adeboye, Nureni Olawale, Adeboye, Paul, Rimi, Paul, Rimi, Purkait, Souvik, Purkait, Souvik, Saha, Gokul, Saha, Gokul, Samadder, Swetadri, Samadder, Swetadri, Sengupta, Anindita, Sengupta, Anindita, Sharma, Vivek, Sharma, Vivek, Singh, Vijai, Singh, Vijai, |
author_facet |
Acharjee, Santanu, Acharjee, Santanu, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Bhattacharyya, Souvik, Chaudhuri, Dipta, Chaudhuri, Dipta, Dawud Adebayo, Agunbiade, Dawud Adebayo, Agunbiade, Ghosh, Koushik, Ghosh, Koushik, Ghosh, Koushik, Ghosh, Koushik, Indu, Pabak, Indu, Pabak, Khan, Samarpita, Khan, Samarpita, Khondekar, Mofazzal H., Khondekar, Mofazzal H., Mukherjee, Moloy, Mukherjee, Moloy, Nureni Olawale, Adeboye, Nureni Olawale, Adeboye, Paul, Rimi, Paul, Rimi, Purkait, Souvik, Purkait, Souvik, Saha, Gokul, Saha, Gokul, Samadder, Swetadri, Samadder, Swetadri, Sengupta, Anindita, Sengupta, Anindita, Sharma, Vivek, Sharma, Vivek, Singh, Vijai, Singh, Vijai, |
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author_sort |
Acharjee, Santanu, |
title |
Noise Filtering for Big Data Analytics / |
spellingShingle |
Noise Filtering for Big Data Analytics / De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , Frontmatter -- Preface -- Contents -- About the Editors -- Application of discrete domain wavelet filter for signal denoising -- Secret sharing scheme in defense and big data analytics -- Recent advances in digital image smoothing: A review -- Double exponential smoothing and its tuning parameters: A re-exploration -- Effect of smoothing on big data governed by polynomial memory -- Heteroskedasticity in panel data: A big challenge to data filtering -- Importance and use of digital filters in digital image processing -- Smart filter and smoothing: A new approach of data denoising -- Acknowledgement -- Index |
title_full |
Noise Filtering for Big Data Analytics / ed. by Souvik Bhattacharyya, Koushik Ghosh. |
title_fullStr |
Noise Filtering for Big Data Analytics / ed. by Souvik Bhattacharyya, Koushik Ghosh. |
title_full_unstemmed |
Noise Filtering for Big Data Analytics / ed. by Souvik Bhattacharyya, Koushik Ghosh. |
title_auth |
Noise Filtering for Big Data Analytics / |
title_alt |
Frontmatter -- Preface -- Contents -- About the Editors -- Application of discrete domain wavelet filter for signal denoising -- Secret sharing scheme in defense and big data analytics -- Recent advances in digital image smoothing: A review -- Double exponential smoothing and its tuning parameters: A re-exploration -- Effect of smoothing on big data governed by polynomial memory -- Heteroskedasticity in panel data: A big challenge to data filtering -- Importance and use of digital filters in digital image processing -- Smart filter and smoothing: A new approach of data denoising -- Acknowledgement -- Index |
title_new |
Noise Filtering for Big Data Analytics / |
title_sort |
noise filtering for big data analytics / |
series |
De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , |
series2 |
De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , |
publisher |
De Gruyter, |
publishDate |
2022 |
physical |
1 online resource (VIII, 156 p.) Issued also in print. |
contents |
Frontmatter -- Preface -- Contents -- About the Editors -- Application of discrete domain wavelet filter for signal denoising -- Secret sharing scheme in defense and big data analytics -- Recent advances in digital image smoothing: A review -- Double exponential smoothing and its tuning parameters: A re-exploration -- Effect of smoothing on big data governed by polynomial memory -- Heteroskedasticity in panel data: A big challenge to data filtering -- Importance and use of digital filters in digital image processing -- Smart filter and smoothing: A new approach of data denoising -- Acknowledgement -- Index |
isbn |
9783110697216 9783110766820 9783110993899 9783110994810 9783110994223 9783110994193 9783110697261 9783110697094 |
issn |
2626-5427 ; |
url |
https://doi.org/10.1515/9783110697216 https://www.degruyter.com/isbn/9783110697216 https://www.degruyter.com/document/cover/isbn/9783110697216/original |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
004 - Data processing & computer science |
dewey-full |
004 |
dewey-sort |
14 |
dewey-raw |
004 |
dewey-search |
004 |
doi_str_mv |
10.1515/9783110697216 |
oclc_num |
1328137295 |
work_keys_str_mv |
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n |
ids_txt_mv |
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cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2022 English Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2022 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2022 English Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2022 |
is_hierarchy_title |
Noise Filtering for Big Data Analytics / |
container_title |
Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1 |
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