Big data analytics: theory, techniques, platforms, and applications
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2024]
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| Schlagwörter: | |
| Abstract: | This book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks. The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world |
| Beschreibung: | Introduction.- Big Data.- Big Data Analytics.- Cloud Computing for Big Data Analytics.- Big Data Analytics Platforms.- Big Data Storage Solutions.- Big Data Monitoring.- Debugging Big Data Systems for Big Data Analytics.- Machine Learning for Big Data Analytics.- Real-World Big Data Analytics Case Studies.- Big Data Analytics in Smart Grids.- Big Data Analytics in Bioinformatics |
| Umfang: | xxiii, 284 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031556388 9783031566981 |
Internformat
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| 500 | |a Introduction.- Big Data.- Big Data Analytics.- Cloud Computing for Big Data Analytics.- Big Data Analytics Platforms.- Big Data Storage Solutions.- Big Data Monitoring.- Debugging Big Data Systems for Big Data Analytics.- Machine Learning for Big Data Analytics.- Real-World Big Data Analytics Case Studies.- Big Data Analytics in Smart Grids.- Big Data Analytics in Bioinformatics | ||
| 520 | 3 | |a This book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks. The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world | |
| 653 | 0 | |a Data capture & analysis | |
| 653 | 0 | |a Datenerfassung und -analyse | |
| 653 | 0 | |a Energieerzeugung und -verteilung | |
| 653 | 0 | |a Gesundheitsfachberufe | |
| 653 | 0 | |a MEDICAL / Allied Health Services / General | |
| 653 | 0 | |a MEDICAL / Nursing / General | |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a Maschinelles Lernen | |
| 653 | 0 | |a Nursing & ancillary services | |
| 653 | 0 | |a Power generation & distribution | |
| 700 | 1 | |a Aujla, Gagangeet Singh |e Verfasser |4 aut | |
| 700 | 1 | |a Jindal, Anish |e Verfasser |4 aut | |
| 700 | 1 | |a Kalyon, Oğuzhan |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-031-55639-5 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035037198 | |
Datensatz im Suchindex
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| author | Demirbaga, Ümit Aujla, Gagangeet Singh Jindal, Anish Kalyon, Oğuzhan |
| author_facet | Demirbaga, Ümit Aujla, Gagangeet Singh Jindal, Anish Kalyon, Oğuzhan |
| author_role | aut aut aut aut |
| author_sort | Demirbaga, Ümit |
| author_variant | ü d üd g s a gs gsa a j aj o k ok |
| building | Verbundindex |
| bvnumber | BV049694676 |
| ctrlnum | (OCoLC)1433325818 (DE-599)BVBBV049694676 |
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| id | DE-604.BV049694676 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T20:19:18Z |
| institution | BVB |
| isbn | 9783031556388 9783031566981 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035037198 |
| oclc_num | 1433325818 |
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| owner_facet | DE-522 DE-29 |
| physical | xxiii, 284 Seiten Illustrationen, Diagramme |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| record_format | marc |
| spelling | Demirbaga, Ümit Verfasser aut Big data analytics theory, techniques, platforms, and applications Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon Cham, Switzerland Springer [2024] xxiii, 284 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Introduction.- Big Data.- Big Data Analytics.- Cloud Computing for Big Data Analytics.- Big Data Analytics Platforms.- Big Data Storage Solutions.- Big Data Monitoring.- Debugging Big Data Systems for Big Data Analytics.- Machine Learning for Big Data Analytics.- Real-World Big Data Analytics Case Studies.- Big Data Analytics in Smart Grids.- Big Data Analytics in Bioinformatics This book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks. The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world Data capture & analysis Datenerfassung und -analyse Energieerzeugung und -verteilung Gesundheitsfachberufe MEDICAL / Allied Health Services / General MEDICAL / Nursing / General Machine learning Maschinelles Lernen Nursing & ancillary services Power generation & distribution Aujla, Gagangeet Singh Verfasser aut Jindal, Anish Verfasser aut Kalyon, Oğuzhan Verfasser aut Erscheint auch als Online-Ausgabe 978-3-031-55639-5 |
| spellingShingle | Demirbaga, Ümit Aujla, Gagangeet Singh Jindal, Anish Kalyon, Oğuzhan Big data analytics theory, techniques, platforms, and applications |
| title | Big data analytics theory, techniques, platforms, and applications |
| title_auth | Big data analytics theory, techniques, platforms, and applications |
| title_exact_search | Big data analytics theory, techniques, platforms, and applications |
| title_full | Big data analytics theory, techniques, platforms, and applications Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon |
| title_fullStr | Big data analytics theory, techniques, platforms, and applications Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon |
| title_full_unstemmed | Big data analytics theory, techniques, platforms, and applications Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon |
| title_short | Big data analytics |
| title_sort | big data analytics theory techniques platforms and applications |
| title_sub | theory, techniques, platforms, and applications |
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