大葉大學圖書館 |

Big data analytics[electronic resource] :methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	005.7
書名/作者:	Big data analytics : methods and applications // edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao.
其他作者:	Rao, B.L.S. Prakasa.
出版者:	New Delhi : : Springer India :, 2016.
面頁冊數:	xii, 276 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Big data.
標題:	Statistics.
標題:	Statistics and Computing/Statistics Programs.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
標題:	Statistics for Business/Economics/Mathematical Finance/Insurance.
標題:	Data Mining and Knowledge Discovery.
標題:	Applications of Mathematics.
ISBN:	9788132236283
ISBN:	9788132236269
內容註:	Chapter 1. Introduction: The Promises and Challenges of Big Data Analytics -- Chapter 2. Massive Data Analysis: Tasks, Tools, Applications and Challenges -- Chapter 3. Statistical Challenges with Big Data in Management Science -- Chapter 4. Application of Mixture Models to Large Datasets -- Chapter 5. An Efficient Partition-Repetition Approach in Clustering of Big Data -- Chapter 6. Multithreaded Graph Algorithms for Large-scale Analytics -- Chapter 7. On-line Graph Partitioning with an Affine Message Combining Cost Function -- Chapter 8. Big Data Analytics Platforms for Real-time Applications in IoT -- Chapter 9. Complex Event Processing in Big Data Systems -- Chapter 10. Unwanted Traffic Identification in Large-scale University Networks: A Case Study -- Chapter 11. Application-Level Benchmarking of Big Data Systems -- Chapter 12. Managing Large Scale Standardized Electronic Healthcare Records -- Chapter 13. Microbiome Data Mining for Microbial Interactions and Relationships -- Chapter 14. A Nonlinear Technique for Analysis of Big Data in Neuroscience -- Chapter 15. Big Data and Cancer Research.
摘要、提要註:	This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
電子資源:	http://dx.doi.org/10.1007/978-81-322-3628-3

Big data analytics[electronic resource] :methods and applications /
Big data analyticsmethods and applications /[electronic resource] :edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao. - New Delhi :Springer India :2016. - xii, 276 p. :ill., digital ;24 cm.

Chapter 1. Introduction: The Promises and Challenges of Big Data Analytics -- Chapter 2. Massive Data Analysis: Tasks, Tools, Applications and Challenges -- Chapter 3. Statistical Challenges with Big Data in Management Science -- Chapter 4. Application of Mixture Models to Large Datasets -- Chapter 5. An Efficient Partition-Repetition Approach in Clustering of Big Data -- Chapter 6. Multithreaded Graph Algorithms for Large-scale Analytics -- Chapter 7. On-line Graph Partitioning with an Affine Message Combining Cost Function -- Chapter 8. Big Data Analytics Platforms for Real-time Applications in IoT -- Chapter 9. Complex Event Processing in Big Data Systems -- Chapter 10. Unwanted Traffic Identification in Large-scale University Networks: A Case Study -- Chapter 11. Application-Level Benchmarking of Big Data Systems -- Chapter 12. Managing Large Scale Standardized Electronic Healthcare Records -- Chapter 13. Microbiome Data Mining for Microbial Interactions and Relationships -- Chapter 14. A Nonlinear Technique for Analysis of Big Data in Neuroscience -- Chapter 15. Big Data and Cancer Research.

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

ISBN: 9788132236283

Standard No.: 10.1007/978-81-322-3628-3doiSubjects--Topical Terms:

571002
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data analytics[electronic resource] :methods and applications /
LDR:02942nmm a2200313 a 4500 001 467674
003 DE-He213
005 20161012160412.0
006 m d
007 cr nn 008maaau
008 170511s2016 ii s 0 eng d
020 $a 9788132236283 $q (electronic bk.)
020 $a 9788132236269 $q (paper)
024 7 $a 10.1007/978-81-322-3628-3 $2 doi
035 $a 978-81-322-3628-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b B592 2016
245 0 0 $a Big data analytics $h [electronic resource] : $b methods and applications / $c edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao.
260 $a New Delhi : $b Springer India : $b Imprint: Springer, $c 2016.
300 $a xii, 276 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Introduction: The Promises and Challenges of Big Data Analytics -- Chapter 2. Massive Data Analysis: Tasks, Tools, Applications and Challenges -- Chapter 3. Statistical Challenges with Big Data in Management Science -- Chapter 4. Application of Mixture Models to Large Datasets -- Chapter 5. An Efficient Partition-Repetition Approach in Clustering of Big Data -- Chapter 6. Multithreaded Graph Algorithms for Large-scale Analytics -- Chapter 7. On-line Graph Partitioning with an Affine Message Combining Cost Function -- Chapter 8. Big Data Analytics Platforms for Real-time Applications in IoT -- Chapter 9. Complex Event Processing in Big Data Systems -- Chapter 10. Unwanted Traffic Identification in Large-scale University Networks: A Case Study -- Chapter 11. Application-Level Benchmarking of Big Data Systems -- Chapter 12. Managing Large Scale Standardized Electronic Healthcare Records -- Chapter 13. Microbiome Data Mining for Microbial Interactions and Relationships -- Chapter 14. A Nonlinear Technique for Analysis of Big Data in Neuroscience -- Chapter 15. Big Data and Cancer Research.
520 $a This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
650 0 $a Big data. $3 571002
650 1 4 $a Statistics. $3 145349
650 2 4 $a Statistics and Computing/Statistics Programs. $3 463725
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 464109
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 464573
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 464928
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
650 2 4 $a Applications of Mathematics. $3 463820
700 1 $a Rao, B.L.S. Prakasa. $3 673095
700 1 $a Rao, S.B. $3 673096
700 1 $a Pyne, Saumyadipta. $3 673094
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-81-322-3628-3
950 $a Mathematics and Statistics (Springer-11649)