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Integration challenges for analytics...

Azevedo, Ana.

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Integration challenges for analytics, business intelligence, and data mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integration challenges for analytics, business intelligence, and data mining/ Ana Azevedo and Manuel Filipe Santos, editors.
其他作者:	Azevedo, Ana.
出版者:	Hershey, Pennsylvania :IGI Global, : 2020.,
面頁冊數:	1 online resource (xix, 250 p.)
內容註:	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.
標題:	Business enterprises - Data processing. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-5781-5
ISBN:	9781799857839 (ebk.)

Integration challenges for analytics, business intelligence, and data mining
Integration challenges for analytics, business intelligence, and data mining[electronic resource] /Ana Azevedo and Manuel Filipe Santos, editors. - Hershey, Pennsylvania :IGI Global,2020. - 1 online resource (xix, 250 p.)

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.

"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"--

ISBN: 9781799857839 (ebk.)Subjects--Topical Terms:

750112
Business enterprises
--Data processing.

LC Class. No.: HF5548.2 / .I58 2020

Dewey Class. No.: 658.4/72

Integration challenges for analytics, business intelligence, and data mining
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245 0 0 $a Integration challenges for analytics, business intelligence, and data mining $h [electronic resource] / $c Ana Azevedo and Manuel Filipe Santos, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2020.
300 $a 1 online resource (xix, 250 p.)
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.
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.
650 0 $a Business enterprises $x Data processing. $3 750112
650 0 $a Business intelligence. $3 669648
650 0 $a Data mining. $3 562972
700 1 $a Azevedo, Ana. $3 3510817
700 1 $a Santos, Manuel Filipe. $3 3510818
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