國立虎尾科技大學 |

Integration challenges for analytics, business intelligence, and data mining

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integration challenges for analytics, business intelligence, and data mining/ Ana Azevedo and Manuel Filipe Santos, editors.
other author:	Azevedo, Ana.
Published:	Hershey, Pennsylvania :IGI Global, : 2020.,
Description:	1 online resource (xix, 250 p.)
Subject:	Business enterprises - Data processing. -
Online resource:	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:

562959
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
LDR:02256nam a2200265 a 4500 001 1041439
003 IGIG
005 20211027160441.0
006 m o d
007 cr cn
008 211215s2020 pau fob 001 0 eng d
020 $a 9781799857839 (ebk.)
020 $a 9781799857815 (hbk.)
020 $a 9781799857822 (pbk.)
035 $a (OCoLC)1224336025
035 $a 1101012310
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 0 0 $a HF5548.2 $b .I58 2020
082 0 0 $a 658.4/72 $2 23
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 562959
650 0 $a Business intelligence. $3 557121
650 0 $a Data mining. $3 528622
700 1 $a Azevedo, Ana. $3 1340956
700 1 $a Santos, Manuel Filipe. $3 1340957
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-5781-5