Utilizing big data paradigms for business intelligence:
"This book explores problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. It also covers how data must be extracted, grouped, organized, aggregated and correlated with methods and techniques such as data integration (ETL), dat...
Saved in:
| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
[2018]
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | "This book explores problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. It also covers how data must be extracted, grouped, organized, aggregated and correlated with methods and techniques such as data integration (ETL), data warehousing, online analytical processing (OLAP), reporting, data mining and machine learning"-- |
| Item Description: | Includes index. |
| Physical Description: | 19 PDFs (xxii, 313 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781522549642 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
Staff View
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| 245 | 0 | 0 | |a Utilizing big data paradigms for business intelligence |c Jerome Darmont and Sabine Loudcher, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c [2018] | |
| 300 | |a 19 PDFs (xxii, 313 Seiten) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Chapter 1. Applications of artificial intelligence in the realm of business intelligence -- Chapter 2. A big data platform for enhancing life imaging activities -- Chapter 3. A Survey of parallel indexing techniques for large-scale moving object databases -- Chapter 4. Privacy and security in data-driven urban mobility -- Chapter 5. C-idea: a fast algorithm for computing emerging closed datacubes -- Chapter 6. Large multivariate time series forecasting: Survey on methods and scalability -- Chapter 7. Exploring multiple dynamic social networks in computer-mediated communications: an experimentally validated ecosystem -- Chapter 8. Analysis of operation performance of blast furnace with machine learning methods. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "This book explores problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. It also covers how data must be extracted, grouped, organized, aggregated and correlated with methods and techniques such as data integration (ETL), data warehousing, online analytical processing (OLAP), reporting, data mining and machine learning"-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | 0 | |a Description based on title screen (IGI Global, viewed 07/17/2018). | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Business intelligence |x Data processing. | |
| 653 | |a Data Mining. | ||
| 653 | |a Data Privacy. | ||
| 653 | |a Data Warehouses. | ||
| 653 | |a Heterogeneous Computing. | ||
| 653 | |a Machine Learning. | ||
| 653 | |a Mobility. | ||
| 653 | |a Multi-User Communication. | ||
| 653 | |a Trajectory Clustering. | ||
| 653 | |a Trend Analysis. | ||
| 700 | 1 | |a Darmont, Jérôme |d 1972- |e editor. | |
| 700 | 1 | |a Loudcher, Sabine |d 1969- |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
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| contents | Chapter 1. Applications of artificial intelligence in the realm of business intelligence -- Chapter 2. A big data platform for enhancing life imaging activities -- Chapter 3. A Survey of parallel indexing techniques for large-scale moving object databases -- Chapter 4. Privacy and security in data-driven urban mobility -- Chapter 5. C-idea: a fast algorithm for computing emerging closed datacubes -- Chapter 6. Large multivariate time series forecasting: Survey on methods and scalability -- Chapter 7. Exploring multiple dynamic social networks in computer-mediated communications: an experimentally validated ecosystem -- Chapter 8. Analysis of operation performance of blast furnace with machine learning methods. |
| ctrlnum | (CaBNVSL)slc20076693 (OCoLC)1045082196 |
| dewey-full | 658.4/72028557 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
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| dewey-search | 658.4/72028557 |
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| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00185737 |
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| indexdate | 2025-03-18T14:30:29Z |
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| isbn | 9781522549642 |
| language | English |
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| spelling | Utilizing big data paradigms for business intelligence Jerome Darmont and Sabine Loudcher, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global [2018] 19 PDFs (xxii, 313 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Includes index. Includes bibliographical references and index. Chapter 1. Applications of artificial intelligence in the realm of business intelligence -- Chapter 2. A big data platform for enhancing life imaging activities -- Chapter 3. A Survey of parallel indexing techniques for large-scale moving object databases -- Chapter 4. Privacy and security in data-driven urban mobility -- Chapter 5. C-idea: a fast algorithm for computing emerging closed datacubes -- Chapter 6. Large multivariate time series forecasting: Survey on methods and scalability -- Chapter 7. Exploring multiple dynamic social networks in computer-mediated communications: an experimentally validated ecosystem -- Chapter 8. Analysis of operation performance of blast furnace with machine learning methods. Restricted to subscribers or individual electronic text purchasers. "This book explores problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. It also covers how data must be extracted, grouped, organized, aggregated and correlated with methods and techniques such as data integration (ETL), data warehousing, online analytical processing (OLAP), reporting, data mining and machine learning"-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 07/17/2018). Big data. Business intelligence Data processing. Data Mining. Data Privacy. Data Warehouses. Heterogeneous Computing. Machine Learning. Mobility. Multi-User Communication. Trajectory Clustering. Trend Analysis. Darmont, Jérôme 1972- editor. Loudcher, Sabine 1969- editor. IGI Global, publisher. (Original) (DLC)2017032931 Print version: 1522549633 9781522549635 (DLC) 2017032931 |
| spellingShingle | Utilizing big data paradigms for business intelligence Chapter 1. Applications of artificial intelligence in the realm of business intelligence -- Chapter 2. A big data platform for enhancing life imaging activities -- Chapter 3. A Survey of parallel indexing techniques for large-scale moving object databases -- Chapter 4. Privacy and security in data-driven urban mobility -- Chapter 5. C-idea: a fast algorithm for computing emerging closed datacubes -- Chapter 6. Large multivariate time series forecasting: Survey on methods and scalability -- Chapter 7. Exploring multiple dynamic social networks in computer-mediated communications: an experimentally validated ecosystem -- Chapter 8. Analysis of operation performance of blast furnace with machine learning methods. Big data. Business intelligence Data processing. |
| title | Utilizing big data paradigms for business intelligence |
| title_auth | Utilizing big data paradigms for business intelligence |
| title_exact_search | Utilizing big data paradigms for business intelligence |
| title_full | Utilizing big data paradigms for business intelligence Jerome Darmont and Sabine Loudcher, editors. |
| title_fullStr | Utilizing big data paradigms for business intelligence Jerome Darmont and Sabine Loudcher, editors. |
| title_full_unstemmed | Utilizing big data paradigms for business intelligence Jerome Darmont and Sabine Loudcher, editors. |
| title_short | Utilizing big data paradigms for business intelligence |
| title_sort | utilizing big data paradigms for business intelligence |
| topic | Big data. Business intelligence Data processing. |
| topic_facet | Big data. Business intelligence Data processing. |
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