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: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, PA
IGI Global
[2020]
|
| Schlagworte: | |
| Online-Zugang: | DE-824 DE-1050 DE-83 DE-706 DE-898 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: | 1 Online-Ressource (XXII, 261 Seiten) |
| ISBN: | 9781522597520 |
| DOI: | 10.4018/978-1-5225-9750-6 |
Internformat
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| 245 | 1 | 0 | |a Big data analytics for sustainable computing |c Anandakumar Haldorai, Arulmurugan Ramu |
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| 505 | 8 | |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 | |
| 520 | |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 | ||
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Datensatz im Suchindex
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| author | Haldorai, Anandakumar 1983- Ramu, Arulmurugan 1985- |
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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 | (OCoLC)1126553733 (DE-599)BVBBV046217829 |
| doi_str_mv | 10.4018/978-1-5225-9750-6 |
| format | Electronic eBook |
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| id | DE-604.BV046217829 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T06:01:26Z |
| institution | BVB |
| isbn | 9781522597520 |
| language | English |
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| spelling | Haldorai, Anandakumar 1983- Verfasser (DE-588)1187283177 aut Big data analytics for sustainable computing Anandakumar Haldorai, Arulmurugan Ramu Hershey, PA IGI Global [2020] 1 Online-Ressource (XXII, 261 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 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 Big data Ramu, Arulmurugan 1985- Verfasser (DE-588)1187283657 aut Erscheint auch als Druck-Ausgabe, hardcover 978-1-5225-9750-6 Erscheint auch als Druck-Ausgabe, softcover 978-1-5225-9751-3 https://doi.org/10.4018/978-1-5225-9750-6 Verlag URL des Erstveröffentlichers Volltext |
| 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, Arulmurugan Ramu |
| title_fullStr | Big data analytics for sustainable computing Anandakumar Haldorai, Arulmurugan Ramu |
| title_full_unstemmed | Big data analytics for sustainable computing Anandakumar Haldorai, 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 |
| url | https://doi.org/10.4018/978-1-5225-9750-6 |
| work_keys_str_mv | AT haldoraianandakumar bigdataanalyticsforsustainablecomputing AT ramuarulmurugan bigdataanalyticsforsustainablecomputing |