Big Data Analytics:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier, North Holland
2015
|
| Series: | Handbook of statistics
33 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 372 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9780444634924 |
Staff View
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV042946891 | ||
| 003 | DE-604 | ||
| 005 | 20170426 | ||
| 007 | t | ||
| 008 | 151026s2015 ad|| |||| 00||| eng d | ||
| 020 | |a 9780444634924 |c hbk. |9 978-0-444-63492-4 | ||
| 035 | |a (OCoLC)931989490 | ||
| 035 | |a (DE-599)BVBBV042946891 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1102 |a DE-29T |a DE-210 |a DE-703 |a DE-384 |a DE-739 |a DE-473 |a DE-83 | ||
| 050 | 0 | |a TK3105 | |
| 082 | 0 | |a 621.310285/57 | |
| 084 | |a QH 500 |0 (DE-625)141607: |2 rvk | ||
| 084 | |a SK 840 |0 (DE-625)143261: |2 rvk | ||
| 084 | |a 62-07 |2 msc | ||
| 084 | |a 62-00 |2 msc | ||
| 245 | 1 | 0 | |a Big Data Analytics |c ed. by Venu Govindaraju ... |
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier, North Holland |c 2015 | |
| 300 | |a XVII, 372 S. |b Ill., graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Handbook of statistics |v 33 | |
| 650 | 0 | 7 | |a Big Data |0 (DE-588)4802620-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Big Data |0 (DE-588)4802620-7 |D s |
| 689 | 0 | 1 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | |C b |5 DE-604 | |
| 700 | 1 | |a Govindaraju, Venu |d 1964- |e Sonstige |0 (DE-588)1036336638 |4 oth | |
| 830 | 0 | |a Handbook of statistics |v 33 |w (DE-604)BV000002510 |9 33 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028373168&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-028373168 | ||
Record in the Search Index
| _version_ | 1804175259871477760 |
|---|---|
| adam_text | Contents
Contributors xiii
Preface xvii
A Modeling and Analytics
1. Document Informatics for Scientific Learning
and Accelerated Discovery 3
Venu Govindaraju, ifeoma Nwogu, and Srirangaraj Setlur
1 Introduction 4
1A Sample Use Case 5
2 How Document Informatics Will Aid Materials Discovery 8
2.1 Motivation 9
2.2 Big Data Justification 10
2.3 Challenges of Meta-Learning in Materials Research 11
3 The General Research Framework 12
4 Pilot Implementation 15
4.1 Objective 1: To Design and Develop a Time-Based,
Hierarchical Topic Model 16
4.2 Objective 2: To Implement Algorithms for Extracting
Text from x—y Plots and Tables 20
4.3 Objective 3: To Develop an Interactive, Materials
Network Visualization Tool 23
4.4 Testing and Validation 25
References 26
2. An Introduction to Rare Event Simulation and
Importance Sampling 29
Gino Biondini
1 Introduction: Monte Carlo Methods, Rare Event Simulation, and
Variance Reduction Techniques 29
2 MC Methods and the Problem of Rare Events 31
2.1 MC Estimators 31
2.2 The Problem of Rare Events 34
v
34
34
36
39
41
42
42
43
44
45
49
52
53
57
63
66
69
69
72
72
74
78
78
79
80
80
81
82
83
83
85
86
86
89
89
91
91
Contents
3 Importance Sampling
3.1 Importance-Sampled MC Estimators
3.2 A Simple Example
3.3 The Optimal Biasing Distribution
3.4 Common Biasing Choices and Their Drawbacks
4 Multiple IS
4.1 Multiple IS: General Formulation
4.2 The Balance Heuristics
4.3 Application: Numerical Estimation of Probability Density Functions
5 The Cross-Entropy Method
6 MCMC: Rejection Sampling, the Metropolis Method, and Gibbs Sampling
7 Applications of VRTs to Error Estimation in Optical Fiber Communication Systems
7.1 Polarization-Mode Dispersion
7.2 Noise-Induced Perturbations
8 Large Deviations Theory, Asymptotic Efficiency, and Final Remarks References
A Large-Scale Study of Language Usage as a Cognitive Biometric Trait
Neeti Pokhriyaf, Ifeoma Nwogu, and Venu Covindaraju
1 Introduction
2 Cognitive Fingerprints: Problem Description
3 Data Description
4 Methodology
5 Results
5.1 Evaluating Performance on Different Types of Data
5.2 Evaluating Performance of the Biometric Trait
5.3 Impact of Features
5.4 Using Authors with Different Minimum Number of Blogs
5.5 Varying the Number of Blogs per Author
5.6 Odd Man Out Analysis
6 Discussions
7 Related Work
8 Conclusions and Future Work Acknowledgment References
Customer Selection Utilizing Big Data Analytics
Jungsuk Kwac and Ram Rajagopal
1 Introduction
1.1 Prior Work
1.2 Goal
Contents
VII
2 Methodology 92
2.1 Response Modeling 92
2.2 Customer Selection 96
3 Experiments 102
3.1 Data Description 102
3.2 Response Modeling Result 102
3.3 Customer Selection Result 104
4 Conclusion 105
References 105
Continuous Model Selection for Large-Scale Recommender Systems 107
Simon Chan and Philip Ire leaven
1 Introduction 108
2 Related Work 109
3 Preference Prediction 110
4 Proposed Continuous Modeling 111
4.1 Collaborative Filtering 111
4.2 Update with New Data 113
4.3 Automatic Model Selection 113
5 Experimental Evaluations 114
5.1 DataSet 114
5.2 Evaluation Metrics 114
5.3 Experimental Setup 115
5.4 Effectiveness on the Online Site 11 7
5.5 Effectiveness on Offline Stores 120
6 Conclusion and Future Work 121
References 123
Zero-Knowledge Mechanisms for Private Release of Social Graph Summarization 125
Maryam Shoaran, Alex Thomo, and Jens H. Weber
1 Introduction 125
2 Related Work 127
3 Graph Summarization 128
4 Background on e-Zero-Knowledge Privacy 129
5 ZKP Mechanism for Graph Summarization 131
5.1 Edge (Connection) Privacy 132
6 Evaluation 137
6.1 Parameters Affecting Noise Scale 138
6.2 The Noise 138
7 From Privacy Level to Noise Scale 139
8 Private Probabilistic A-GS 140
8.1 Probabilistic Graphs 141
8.2 Probabilistic Graph Summarization 141
8.3 Zero-Knowledge Private Probabilistic A-GS 142
Contents
» • • VIII
9 Conclusions References
143
143
7.
Distributed Confidence-Weighted Classification on Big Data Platforms
Nemanja Djuric, Mihajlo Grbovic, and Slobodan Vucetic
1 Introduction
2 Classification with Linear SVM Models
2.1 Linear SVM Classifiers
2.2 CW Classification
3 MapReduce Framework for Distributed Computations
3.1 All Reduce Framework
3.2 Spark Framework
4 CW Classification Using MapReduce
4.1 Reducer-Side Optimization of AROW-MR
5 Experiments
5.1 Validation on Synthetic Data
5.2 Ad Latency Problem Description
5.3 Validation on Ad Latency Data
6 Conclusion Acknowledgments References
145
145
148
148
149
150 152 I 53 154 l 55 157 I 58 160 161
165
166 166
B
8.
Applications and Infrastructure
Big Data Applications in Health Sciences and Epidemiology 171
Saumyadipta Pyne, Anile Kumar S. Vullikanti, and Madhav V. Marathe
1 Introduction 172
1.1 Mathematical and Big Data Computational Epidemiology 173
1.2 Organization and Outline 175
2 Mathematical Framework for Epidemiology 177
3 Dynamics and Analysis Problems 181
4 Inference Problems 181
5 Disease Surveillance, Molecular Epidemiology, and Pathogen
Phylodynamics 182
5.1 Disease Surveillance 184
5.2 Forecasting 185
5.3 Molecular Epidemiology 186
5.4 Pathogen Phylodynamics 190
6 High-Performance Synthetic Information Environments and Tools 191
6.1 Synthetic Networks for Epidemiology 191
6.2 Individual and Collective Behaviors 192
6.3 High-Performance Computing Tools 193
6.4 Decision Support Environments 194
Contents ix
7 Summary 196
Acknowledgments 196
References 196
Big Data Driven Natural Language Processing Research and Applications 203
VenkatN. Gudivada, Dhana Rao, and VIjay V. Raghavan
1 Introduction 203
1.1 Emergence of Big Data 204
1.2 Big Data Driven NLP Research and Applications 204
1.3 Organization of the Chapter 205
2 NLP Core Tasks 205
2.1 Statistical Language Modeling 205
2.2 Word Segmentation 210
2.3 POS Tagging 211
2.4 Named Entity Recognition 214
2.5 Parsing 215
3 NLP Applications 218
3.1 Machine Translation 218
3.2 Information Extraction 220
3.3 Topic Modeling 221
3.4 Probabilistic Topic Modeling 222
3.5 Big Data and Topic Modeling 222
3.6 Text Summarization 222
3.7 Document Clustering and Classification 223
3.8 QA and Dialog Systems 224
3.9 Natural Language User Interfaces 225
3.10 Software Tools and Frameworks 226
4 Data Sources for NLP Research 226
4.1 Brown and New York Times Corpora 227
4.2 Google Corpora 227
4.3 Linguistic Data Consortium 227
4.4 British National and Europarl Parallel Corpora 227
4.5 Other Corpora 228
5 Big Data Driven NLP Research and Applications 228
5.1 Memoization 228
5.2 Leveraging Big Data 229
5.3 Streaming Data 229
5.4 Sparseness Problems 230
5.5 Noisy Data 230
5.6 Selecting and Combining Features 230
5.7 Computational Advertising 230
5.8 Multimedia Data 231
5.9 IBM Watson 231
6 Trends and Future Research Directions 231
6.1 Even More NLP Big Data 232
6.2 Better Models with More Data 232
x Contents
6.3 Spatial Knowledge
6.4 NLP Applications 7 Conclusions References
10. Analyzing Big Spatial and Big Spatiotemporal Data: A Case Study of Methods and Applications
Varun Chandola, Ranga Ra/u Vatsavai, Devashish Kumar, and Auroop Ganguly
1 Introduction
2 Algorithms
2.1 SAR Model
2.2 MRF Classifiers
2.3 Gaussian Process Learning
2.4 Mixture Models
3 Applications
3.1 Biomass Monitoring
3.2 Complex Object Recognition
3.3 Climate Change Studies
4 Conclusions References
232
232
233
234
239
239
241
241
242
243 245 248
248
249 251 255 255
11. Experimental Computational Simulation Environments
for Big Data Analytic in Social Sciences 259
Michal Galas
1 Introduction 259
2 Big Data Analytics 260
3 Sociofinancial-Economic Simulations 262
4 Software Infrastructure for Social Sciences 264
4.1 Financial Data Streaming and Market Information Services 264
4.2 Complex Event Processing Engines 264
4.3 Analytical Tools and Business Intelligence 265
4.4 Experimental and Simulation Environments 265
4.5 Trading Platforms for Manual and Algorithmic Traders 266
4.6 Social Media Platforms for Sentiment Analysis 266
5 Market Simulators for Financial Economics Modeling 267
5.1 Simulation Types and Applications 267
6 Statistical Simulations of AT Models 269
7 DRACUS 272
7.1 Model Implementation and Deployment 274
7.2 Simulation Definition and Control 274
7.3 Simulation Executors 274
7.4 Job Distribution in the Cluster 275
7.5 Connectivity Engine and Compressed Data Transition 275
7.6 Data Aggregation in the Environment s Cluster 275
Contents xi
8 Summary 276
References 276
Terabyte-Scale Image Similarity Search 279
Diana Moise and Denis Shestakov
1 Introduction 279
2 Big-Data Processing 281
2.1 Challenges 281
2.2 Trends 282
2.3 Hadoop 283
3 Application Workload (Distributed Indexing + Searching) 284
3.1 Dataset Preparation 284
3.2 Distributed Index Creation 284
3.3 Distributed Search 285
3.4 Dataset 286
3.5 Observations 286
4 Hadoop in Practice 287
4.1 Experimental Platform 287
4.2 Experiments with Default Hadoop Settings 288
5 Large-Scale Hadoop 293
5.1 Adjusting to the Data Size 293
5.2 Hadoop on Heterogeneous Clusters 294
5.3 Dealing with Large-Size Auxiliary Data 296
5.4 Observations 299
6 Conclusion 299
Acknowledgments 300
References 300
Measuring Inter-site Engagement in a Network of Sites 303
Janette Lehmann, Mounia Laimas, and Ricardo Baeza-Yates
1 Introduction 303
2 Related Work 305
2.1 Web Analytics and User Engagement 305
2.2 Browsing Behavior 305
2.3 Network Analysis 306
3 Data, Networks, and Metrics 307
3.1 Provider Networks 307
3.2 Network-Level Metrics 310
3.3 Node-Level Metrics 312
4 Evaluating Inter-site Metrics 314
4.1 Site and Provider Network Rankings 314
4.2 Case Studies 31 6
5 Studying Inter-site Engagement 320
5.1 User Loyalty 320
5.2 Weekdays and Weekend 322
xii Contents
5.3 Returning Traffic 515
5.4 Upstream Traffic 325
6 The Network Effect 326
6.1 Dependencies Between Sites 327
6.2 Network Effect Patterns 328
6.3 Examples of Patterns 329
7 Hyperlink Performance 330
7.1 Variations in the Link Structure 331
7.2 Effect of the Link Structure 333
8 Conclusions 334
9 Future Work 336
Acknowledgments 336
References 336
14. Scaling RDF Triple Stores in Size and Performance:
Modeling SPARQL Queries as Graph Homomorphism Routines 339
Vito Giovanni Caste!iana, Jesse Weaver, Alessandro Morari,
Antonino Tumeo, David Haglin, John Feo, and Oreste Villa
1 Introduction 340
2 SPARQL Queries as Graph Homomorphism Routines 342
2.1 Graph Pattern Matching 342
2.2 Solution Modifiers 346
3 GEMS: Graph Database Engine for Multithreaded Systems 348
3.1 GMT 348
3.2 SGLib 349
3.3 SPARQL-to-C++ Compiler 350
4 Related Work 353
5 Experimental Results 355
5.1 BSBM: Hand-Coded Queries 356
5.2 SP2B Queries 353
6 Conclusions 353
References 3^
Index
363
|
| any_adam_object | 1 |
| author_GND | (DE-588)1036336638 |
| building | Verbundindex |
| bvnumber | BV042946891 |
| callnumber-first | T - Technology |
| callnumber-label | TK3105 |
| callnumber-raw | TK3105 |
| callnumber-search | TK3105 |
| callnumber-sort | TK 43105 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | QH 500 SK 840 |
| ctrlnum | (OCoLC)931989490 (DE-599)BVBBV042946891 |
| dewey-full | 621.310285/57 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.310285/57 |
| dewey-search | 621.310285/57 |
| dewey-sort | 3621.310285 257 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01864nam a2200469 cb4500</leader><controlfield tag="001">BV042946891</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20170426 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">151026s2015 ad|| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780444634924</subfield><subfield code="c">hbk.</subfield><subfield code="9">978-0-444-63492-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)931989490</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042946891</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1102</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-210</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-384</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-83</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK3105</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621.310285/57</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 500</subfield><subfield code="0">(DE-625)141607:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 840</subfield><subfield code="0">(DE-625)143261:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">62-07</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">62-00</subfield><subfield code="2">msc</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Big Data Analytics</subfield><subfield code="c">ed. by Venu Govindaraju ...</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam [u.a.]</subfield><subfield code="b">Elsevier, North Holland</subfield><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVII, 372 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield><subfield code="c">24 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Handbook of statistics</subfield><subfield code="v">33</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="C">b</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Govindaraju, Venu</subfield><subfield code="d">1964-</subfield><subfield code="e">Sonstige</subfield><subfield code="0">(DE-588)1036336638</subfield><subfield code="4">oth</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Handbook of statistics</subfield><subfield code="v">33</subfield><subfield code="w">(DE-604)BV000002510</subfield><subfield code="9">33</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Bamberg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028373168&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-028373168</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV042946891 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:13:27Z |
| institution | BVB |
| isbn | 9780444634924 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028373168 |
| oclc_num | 931989490 |
| open_access_boolean | |
| owner | DE-1102 DE-29T DE-210 DE-703 DE-384 DE-739 DE-473 DE-BY-UBG DE-83 |
| owner_facet | DE-1102 DE-29T DE-210 DE-703 DE-384 DE-739 DE-473 DE-BY-UBG DE-83 |
| physical | XVII, 372 S. Ill., graph. Darst. 24 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier, North Holland |
| record_format | marc |
| series | Handbook of statistics |
| series2 | Handbook of statistics |
| spelling | Big Data Analytics ed. by Venu Govindaraju ... Amsterdam [u.a.] Elsevier, North Holland 2015 XVII, 372 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics 33 Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s DE-604 Data Mining (DE-588)4428654-5 s b DE-604 Govindaraju, Venu 1964- Sonstige (DE-588)1036336638 oth Handbook of statistics 33 (DE-604)BV000002510 33 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028373168&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Big Data Analytics Handbook of statistics Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4428654-5 (DE-588)4123037-1 (DE-588)4143413-4 |
| title | Big Data Analytics |
| title_auth | Big Data Analytics |
| title_exact_search | Big Data Analytics |
| title_full | Big Data Analytics ed. by Venu Govindaraju ... |
| title_fullStr | Big Data Analytics ed. by Venu Govindaraju ... |
| title_full_unstemmed | Big Data Analytics ed. by Venu Govindaraju ... |
| title_short | Big Data Analytics |
| title_sort | big data analytics |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Big Data Data Mining Datenanalyse Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028373168&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
| work_keys_str_mv | AT govindarajuvenu bigdataanalytics |