Big data analytics for sustainable computing:
Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
2019.
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| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative rese. |
| Item Description: | Description based upon print version of record. |
| Physical Description: | 21 PDFs (263 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
Staff View
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| 245 | 1 | 0 | |a Big data analytics for sustainable computing |c Anandakumar Haldorai and Arulmurugan Ramu. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c 2019. | |
| 300 | |a 21 PDFs (263 Seiten) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 500 | |a Description based upon print version of record. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Chapter 1. Understanding big data -- Chapter 2. A detailed study on classification algorithms in big data -- Chapter 3. Big data and analytics -- Chapter 4. Decoding big data analytics for emerging business through data-intensive applications and business intelligence: a review on analytics applications and theoretical aspects -- Chapter 5. Feature selection algorithm using relative odds for data mining classification -- Chapter 6. Social network analysis -- Chapter 7. Role of machine intelligence and big data in remote sensing -- Chapter 8. Provisioning system for application virtualization environments -- Chapter 9. Big data-based spectrum sensing for cognitive radio networks using artificial intelligence -- Chapter 10. Big data analytics in the healthcare industry: an analysis of healthcare applications in machine learning with big data analytics -- Chapter 11. Big data analytics and visualization for food health status determination using bigmart data -- Chapter 12. "Saksham model" performance improvisation using Node capability evaluation in apache hadoop. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative rese. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | 0 | |a Description based on title screen (IGI Global, viewed 10/04/2019). | |
| 650 | 0 | |a Big data. | |
| 653 | |a Cloud Computing. | ||
| 653 | |a Cognitive Analytics. | ||
| 653 | |a Cyber Security. | ||
| 653 | |a Data Filtering. | ||
| 653 | |a Knowledge Engineering. | ||
| 653 | |a Machine Learning. | ||
| 653 | |a Real-Time Data. | ||
| 653 | |a Scalable Data Management. | ||
| 653 | |a Smart Grid. | ||
| 653 | |a Ubiquitous Data. | ||
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Ramu, Arulmurugan |d 1985- |e author. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | 8 | |i Print version: |z 1522597506 |z 9781522597506 |
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| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-862 | ||
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| _version_ | 1826942596539219968 |
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| author | Haldorai, Anandakumar, 1983- Ramu, Arulmurugan 1985- |
| author_facet | Haldorai, Anandakumar, 1983- Ramu, Arulmurugan 1985- |
| author_role | aut aut |
| author_sort | Haldorai, Anandakumar, 1983- |
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| contents | Chapter 1. Understanding big data -- Chapter 2. A detailed study on classification algorithms in big data -- Chapter 3. Big data and analytics -- Chapter 4. Decoding big data analytics for emerging business through data-intensive applications and business intelligence: a review on analytics applications and theoretical aspects -- Chapter 5. Feature selection algorithm using relative odds for data mining classification -- Chapter 6. Social network analysis -- Chapter 7. Role of machine intelligence and big data in remote sensing -- Chapter 8. Provisioning system for application virtualization environments -- Chapter 9. Big data-based spectrum sensing for cognitive radio networks using artificial intelligence -- Chapter 10. Big data analytics in the healthcare industry: an analysis of healthcare applications in machine learning with big data analytics -- Chapter 11. Big data analytics and visualization for food health status determination using bigmart data -- Chapter 12. "Saksham model" performance improvisation using Node capability evaluation in apache hadoop. |
| ctrlnum | (CaBNVSL)slc21129426 (OCoLC)1122548885 |
| dewey-full | 005.7 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.7 |
| dewey-search | 005.7 |
| dewey-sort | 15.7 |
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| discipline | Informatik |
| format | Electronic eBook |
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| genre_facet | Electronic books. |
| id | ZDB-98-IGB-00223458 |
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| indexdate | 2025-03-18T14:30:31Z |
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| language | English |
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| publishDate | 2019 |
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| spelling | Haldorai, Anandakumar, 1983- author. Big data analytics for sustainable computing Anandakumar Haldorai and Arulmurugan Ramu. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2019. 21 PDFs (263 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Description based upon print version of record. Includes bibliographical references and index. Chapter 1. Understanding big data -- Chapter 2. A detailed study on classification algorithms in big data -- Chapter 3. Big data and analytics -- Chapter 4. Decoding big data analytics for emerging business through data-intensive applications and business intelligence: a review on analytics applications and theoretical aspects -- Chapter 5. Feature selection algorithm using relative odds for data mining classification -- Chapter 6. Social network analysis -- Chapter 7. Role of machine intelligence and big data in remote sensing -- Chapter 8. Provisioning system for application virtualization environments -- Chapter 9. Big data-based spectrum sensing for cognitive radio networks using artificial intelligence -- Chapter 10. Big data analytics in the healthcare industry: an analysis of healthcare applications in machine learning with big data analytics -- Chapter 11. Big data analytics and visualization for food health status determination using bigmart data -- Chapter 12. "Saksham model" performance improvisation using Node capability evaluation in apache hadoop. Restricted to subscribers or individual electronic text purchasers. Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative rese. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 10/04/2019). Big data. Cloud Computing. Cognitive Analytics. Cyber Security. Data Filtering. Knowledge Engineering. Machine Learning. Real-Time Data. Scalable Data Management. Smart Grid. Ubiquitous Data. Electronic books. Ramu, Arulmurugan 1985- author. IGI Global, publisher. Print version: 1522597506 9781522597506 |
| spellingShingle | Haldorai, Anandakumar, 1983- Ramu, Arulmurugan 1985- Big data analytics for sustainable computing Chapter 1. Understanding big data -- Chapter 2. A detailed study on classification algorithms in big data -- Chapter 3. Big data and analytics -- Chapter 4. Decoding big data analytics for emerging business through data-intensive applications and business intelligence: a review on analytics applications and theoretical aspects -- Chapter 5. Feature selection algorithm using relative odds for data mining classification -- Chapter 6. Social network analysis -- Chapter 7. Role of machine intelligence and big data in remote sensing -- Chapter 8. Provisioning system for application virtualization environments -- Chapter 9. Big data-based spectrum sensing for cognitive radio networks using artificial intelligence -- Chapter 10. Big data analytics in the healthcare industry: an analysis of healthcare applications in machine learning with big data analytics -- Chapter 11. Big data analytics and visualization for food health status determination using bigmart data -- Chapter 12. "Saksham model" performance improvisation using Node capability evaluation in apache hadoop. Big data. |
| title | Big data analytics for sustainable computing |
| title_auth | Big data analytics for sustainable computing |
| title_exact_search | Big data analytics for sustainable computing |
| title_full | Big data analytics for sustainable computing Anandakumar Haldorai and Arulmurugan Ramu. |
| title_fullStr | Big data analytics for sustainable computing Anandakumar Haldorai and Arulmurugan Ramu. |
| title_full_unstemmed | Big data analytics for sustainable computing Anandakumar Haldorai and Arulmurugan Ramu. |
| title_short | Big data analytics for sustainable computing |
| title_sort | big data analytics for sustainable computing |
| topic | Big data. |
| topic_facet | Big data. Electronic books. |
| work_keys_str_mv | AT haldoraianandakumar bigdataanalyticsforsustainablecomputing AT ramuarulmurugan bigdataanalyticsforsustainablecomputing AT igiglobal bigdataanalyticsforsustainablecomputing |