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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Description based upon print version of record. |
| Beschreibung: | 21 PDFs (263 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
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| 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 | |a Description based on title screen (IGI Global, viewed 10/04/2019). | ||
| 650 | 0 | |a Big data. | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Ramu, Arulmurugan, |e author. | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-98-IGB-00223458 |
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| author | Haldorai, Anandakumar Ramu, Arulmurugan |
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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 |
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| genre | Electronic books. |
| genre_facet | Electronic books. |
| id | ZDB-98-IGB-00223458 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:51:55Z |
| institution | BVB |
| language | English |
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| physical | 21 PDFs (263 pages) Also available in print. |
| psigel | ZDB-98-IGB |
| publishDate | 2019 |
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| publisher | IGI Global, |
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| spelling | Haldorai, Anandakumar, 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 pages) 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. Electronic books. Ramu, Arulmurugan, author. IGI Global, publisher. Print version: 1522597506 9781522597506 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-9750-6 Volltext |
| spellingShingle | Haldorai, Anandakumar Ramu, Arulmurugan 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. |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-9750-6 |
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