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" - Provided by publisher
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| 245 | 1 | 0 | |a Integration challenges for analytics, business intelligence, and data mining |c Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal), Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal) |
| 264 | 1 | |a Hershey PA, USA |b IGI Global, Publisher of Timely Knowledge |c [2021] | |
| 264 | 4 | |c © 2021 | |
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| 490 | 0 | |a A volume in the Advances in business information systems and analytics (ABISA) book series |x 2327-3283 | |
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| 505 | 8 | |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 | |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" - Provided by publisher | ||
| 650 | 4 | |a Business enterprises / Data processing | |
| 650 | 4 | |a Business intelligence | |
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| 650 | 7 | |a Business intelligence |2 fast | |
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| 700 | 1 | |a Santos, Manuel Filipe |4 edt | |
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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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| doi_str_mv | 10.4018/978-1-7998-5781-5 |
| format | Electronic eBook |
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| id | DE-604.BV047035806 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:07:57Z |
| institution | BVB |
| isbn | 9781799857839 |
| issn | 2327-3283 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032442974 |
| oclc_num | 1225884351 |
| open_access_boolean | |
| owner | DE-1050 DE-706 DE-91 DE-BY-TUM DE-898 DE-BY-UBR DE-1049 DE-573 DE-83 |
| owner_facet | DE-1050 DE-706 DE-91 DE-BY-TUM DE-898 DE-BY-UBR DE-1049 DE-573 DE-83 |
| physical | 1 Online-Ressource (xix, 250 Seiten) Illustrationen |
| psigel | ZDB-98-IGB ZDB-1-IGE ZDB-98-IGB FHD01_IGB_Kauf ZDB-98-IGB TUB_EBS_IGB |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | IGI Global, Publisher of Timely Knowledge |
| record_format | marc |
| series2 | A volume in the Advances in business information systems and analytics (ABISA) book series Premier reference source |
| 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 (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal), Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal) |
| title_fullStr | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal), Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal) |
| title_full_unstemmed | Integration challenges for analytics, business intelligence, and data mining Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal), Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal) |
| 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 |
| url | https://doi.org/10.4018/978-1-7998-5781-5 |
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