Integration challenges for analytics, business intelligence, and data mining:
"This book provides insights concerning the integration of data mining in business intelligence and analytics systems, increasing the understanding of using data mining in the context of business intelligence and analytics"--
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This book provides insights concerning the integration of data mining in business intelligence and analytics systems, increasing the understanding of using data mining in the context of business intelligence and analytics"-- |
| Beschreibung: | 22 PDFs (250 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781799857839 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
MARC
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| 245 | 0 | 0 | |a Integration challenges for analytics, business intelligence, and data mining |c Ana Azevedo and Manuel Filipe Santos, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : |b IGI Global, |c 2020. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Section 1. Background and literature review. Chapter 1. Data mining and business intelligence: a bibliometric analysis ; Chapter 2. Integration of data mining and business intelligence in big data analytics: a research agenda on scholarly publications ; Chapter 3. From business intelligence to big data: the power of analytics -- Section 2. Big data issues. Chapter 4. Big data quality for data mining in business intelligence applications: current state and research directions ; Chapter 5. Enterprise data lake management in business intelligence and analytics: challenges and research gaps in analytics practices and integration -- Section 3. Modelling issues. Chapter 6. Modelling in support of decision making in business intelligence ; Chapter 7. Causal feature selection ; Chapter 8. K-nearest neighbors algorithm (KNN): an approach to detect illicit transaction in the bitcoin network -- Section 4. Software and security. Chapter 9. A framework to evaluate big data fabric tools ; Chapter 10. A novel approach using steganography and cryptography in business intelligence. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "This book provides insights concerning the integration of data mining in business intelligence and analytics systems, increasing the understanding of using data mining in the context of business intelligence and analytics"-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 11/28/2020). | ||
| 650 | 0 | |a Business enterprises |x Data processing. | |
| 650 | 0 | |a Business intelligence. | |
| 650 | 0 | |a Data mining. | |
| 650 | 7 | |a Business enterprises |x Data processing |2 fast | |
| 650 | 7 | |a Business intelligence |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 700 | 1 | |a Azevedo, Ana, |e editor. | |
| 700 | 1 | |a Santos, Manuel Filipe, |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | |c (Original) |w (DLC)2020018690 | |
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| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
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| contents | Section 1. Background and literature review. Chapter 1. Data mining and business intelligence: a bibliometric analysis ; Chapter 2. Integration of data mining and business intelligence in big data analytics: a research agenda on scholarly publications ; Chapter 3. From business intelligence to big data: the power of analytics -- Section 2. Big data issues. Chapter 4. Big data quality for data mining in business intelligence applications: current state and research directions ; Chapter 5. Enterprise data lake management in business intelligence and analytics: challenges and research gaps in analytics practices and integration -- Section 3. Modelling issues. Chapter 6. Modelling in support of decision making in business intelligence ; Chapter 7. Causal feature selection ; Chapter 8. K-nearest neighbors algorithm (KNN): an approach to detect illicit transaction in the bitcoin network -- Section 4. Software and security. Chapter 9. A framework to evaluate big data fabric tools ; Chapter 10. A novel approach using steganography and cryptography in business intelligence. |
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| dewey-full | 658.4/72 |
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| indexdate | 2024-11-26T14:51:57Z |
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| isbn | 9781799857839 |
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| spelling | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo and Manuel Filipe Santos, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, 2020. 22 PDFs (250 pages) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Section 1. Background and literature review. Chapter 1. Data mining and business intelligence: a bibliometric analysis ; Chapter 2. Integration of data mining and business intelligence in big data analytics: a research agenda on scholarly publications ; Chapter 3. From business intelligence to big data: the power of analytics -- Section 2. Big data issues. Chapter 4. Big data quality for data mining in business intelligence applications: current state and research directions ; Chapter 5. Enterprise data lake management in business intelligence and analytics: challenges and research gaps in analytics practices and integration -- Section 3. Modelling issues. Chapter 6. Modelling in support of decision making in business intelligence ; Chapter 7. Causal feature selection ; Chapter 8. K-nearest neighbors algorithm (KNN): an approach to detect illicit transaction in the bitcoin network -- Section 4. Software and security. Chapter 9. A framework to evaluate big data fabric tools ; Chapter 10. A novel approach using steganography and cryptography in business intelligence. Restricted to subscribers or individual electronic text purchasers. "This book provides insights concerning the integration of data mining in business intelligence and analytics systems, increasing the understanding of using data mining in the context of business intelligence and analytics"-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 11/28/2020). Business enterprises Data processing. Business intelligence. Data mining. Business enterprises Data processing fast Business intelligence fast Data mining fast Azevedo, Ana, editor. Santos, Manuel Filipe, editor. IGI Global, publisher. (Original) (DLC)2020018690 Print version: 1799857816 9781799857815 (DLC) 2020018690 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-5781-5 Volltext |
| spellingShingle | Integration challenges for analytics, business intelligence, and data mining Section 1. Background and literature review. Chapter 1. Data mining and business intelligence: a bibliometric analysis ; Chapter 2. Integration of data mining and business intelligence in big data analytics: a research agenda on scholarly publications ; Chapter 3. From business intelligence to big data: the power of analytics -- Section 2. Big data issues. Chapter 4. Big data quality for data mining in business intelligence applications: current state and research directions ; Chapter 5. Enterprise data lake management in business intelligence and analytics: challenges and research gaps in analytics practices and integration -- Section 3. Modelling issues. Chapter 6. Modelling in support of decision making in business intelligence ; Chapter 7. Causal feature selection ; Chapter 8. K-nearest neighbors algorithm (KNN): an approach to detect illicit transaction in the bitcoin network -- Section 4. Software and security. Chapter 9. A framework to evaluate big data fabric tools ; Chapter 10. A novel approach using steganography and cryptography in business intelligence. Business enterprises Data processing. Business intelligence. Data mining. Business enterprises Data processing fast Business intelligence fast Data mining fast |
| title | Integration challenges for analytics, business intelligence, and data mining |
| title_auth | Integration challenges for analytics, business intelligence, and data mining |
| title_exact_search | Integration challenges for analytics, business intelligence, and data mining |
| title_full | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo and Manuel Filipe Santos, editors. |
| title_fullStr | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo and Manuel Filipe Santos, editors. |
| title_full_unstemmed | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo and Manuel Filipe Santos, editors. |
| title_short | Integration challenges for analytics, business intelligence, and data mining |
| title_sort | integration challenges for analytics business intelligence and data mining |
| topic | Business enterprises Data processing. Business intelligence. Data mining. Business enterprises Data processing fast Business intelligence fast Data mining fast |
| topic_facet | Business enterprises Data processing. Business intelligence. Data mining. Business enterprises Data processing Business intelligence Data mining |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-5781-5 |
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