Big data over networks:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2016
|
| Ausgabe: | First published |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xx, 438 pages Diagramme 26 cm |
| ISBN: | 9781107099005 |
Internformat
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Datensatz im Suchindex
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| adam_text | Cambridge
UNIVERSITY PRESS
University Printing House, Cambridge CB2 8BS, United Kingdom
Cambridge University Press is part of the University of Cambridge.
It furthers the University’s mission by disseminating knowledge in the pursuit of
education, learning and research at the highest international levels of excellence.
www.cambridge.org
Information on this title: www.cambridge.org/9781107099005
© Cambridge University Press 2016
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2016
Printed in the United Kingdom by TJ International Ltd. Pad stow Cornwall
A catalog record for this publication is available from the British Library
ISBN 978-1-107-09900-5 Hardback
Cambridge University Press has no responsibility for the persistence or accuracy
of URLs for external or third-party internet websites referred to in this publication,
and does not guarantee that any content on such websites is, or will remain,
accurate or appropriate.
Contents
List of contributors page xiii
Preface xvii
Part I Mathematical foundations l
1 Tensor models: solution methods and applications 3
Shiqian Ma, Bo Jiang, Xiuzhen Huang, and Shuzhong Zhang
1.1 Introduction 3
1.2 Tensor models 5
1.2.1 Sparse and low-rank tensor optimization models 5
1.2.2 Tensor principal component analysis 6
1.2.3 The tensor co-clustering problem 8
1.3 Reformulation of tensor models 11
1.3.1 Low~n -rank tensor optimization 11
1.3.2 Equivalent formulation of tensor PC A 13
1.4 Solution methods 16
1.4.1 Directly resorting to some existing solver 16
1.4.2 First-order methods 18
1.4.3 The block optimization technique 22
1.5 Applications 24
1.5.1 Computational results on gene expression data 25
1.6 Conclusions 30
References 31
2 Sparsity-aware distributed learning 37
Symeon Chouvardas, Yannis Kopsinis, and Sergios Theodoridis
2.1 Introduction 37
2.2 Batch distributed sparsity promoting algorithms 39
2.2.1 Problem formulation 39
2.2.2 LASSO and its distributed learning formulation 40
2.2.3 Sparsity-aware learning: the greedy point of view 42
2.2.4 Other distributed sparse recovery algorithms 45
vi Contents
A ^ .. ..
2.3 Online sparsity-aware distributed learning 46
2.3.1 Problem description 46
2.3.2 LMS based sparsity-promoting algorithm 47
2.3.3 The GreeDi LMS algorithm 49
2.3.4 Set-theoretic sparsity-aware distributed learning 51
2.4 Simulation examples 56
2.4.1 Performance evaluation of batch methods 57
2.4.2 Performance evaluation of online methods 58
References 61
3 Optimization algorithms for big data with application in wireless networks 66
Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun, and Zhi-Quan Luo
3.1 Introduction 66
3.1.1 Motivation 66
3.1.2 The organization of the chapter 67
3.2 First-order algorithms for big data 67
3.2.1 The block coordinate descent algorithm 67
3.2.2 The ADMM algorithm 69
3.2.3 The RSUM method 70
3.3 Application to network provisioning problem 72
3.3.1 The setting 72
3.3.2 Network with an uncapacitated backhaul 75
3.3.3 Network with a capacitated backhaul 82
3.4 Numerical results 88
3.4.1 Scenario 1: Performance comparison with heuristic
algorithms 89
3.4.2 Scenario 2: The efficiency of N-MaxMin WMMSE algorithm 91
3.4.3 Scenario 3: Multi-commodity routing problem with parallel
implementation 92
3.4.4 Scenario 4: Performance evaluation for Algorithm 1 with zones
of nodes 94
3.5 Appendix 94
References 97
4 A unified distributed algorithm for non-cooperative games q j
Jong-Shi Pang and Meisam Razaviyayn
4.1 Introduction ^
4.2 The nonsmooth, nonconvex game 104
4.3 The unified algorithm
4.3.1 Special cases
4.4 Convergence analysis: contraction approach I j 0
4.4.1 Probabilistic player choices 1 t ^
Contents vii
4.5 Convergence analysis: potential approach 116
4.5.1 Generalized potential games 121
References 122
Appendix 125
Part II Big data over cyber networks 135
5 Big data analytics systems 137
Ganesh Ananthanarayanan and Ishai Menache
5.1 Introduction 137
5.2 Scheduling 139
5.2.1 Fairness 139
5.2.2 Placement constraints 142
5.2.3 Additional system-wide objectives 145
5.2.4 Stragglers 146
5.3 Storage 148
5.3.1 Distributed file system 148
5.3.2 In-memory storage 151
5.4 Concluding remarks 156
References 158
6 Distributed big data storage in optical wireless networks 161
Chen Gong, Zhengyuan Xu, and Xiaodong Wang
6.1 Introduction 161
6.2 Big data distributed storage in a wireless network 163
6.2.1 Wireless distributed storage network framework 163
6.2.2 Optical wireless framework 165
6.2.3 Rateless coded distributed data storage 167
6.2.4 Network coded system with full downloading 167
6.2.5 Network coded system with partial downloading 168
6.3 Reconstructability condition for partial downloading 169
6.3.1 jt-Reconstructability for MSR point 169
6.3.2 ¿¿-Reconstructability for a practical MBR point coding
scheme 170
6.4 Channel and power allocation for partial downloading 173
6.4.1 Wireless resource allocation framework 174
6.4.2 Optimal channel and power allocation for the relaxed problem 174
6.5 Open research topics 176
6.5.1 General research topics for wireless distributed storage networks 176
6.5.2 Research topics for data storage in optical wireless networks 177
6.5.3 Research topics for data storage in named data networks 177
References 178
vüi Contents
7 Big data aware wireless communication: challenges and opportunities 180
Suzhi Bi, Rui Zhang, Zhi Ding, and Shuguang Cui
7.1 Introduction 180
7.2 Scalable wireless network architecture for big data 182
7.2.1 Hybrid processing structure 182
7.2.2 Web caching in wireless infrastructure 185
7.2.3 Data aware processing units 187
7.3 Wireless system design in big data era 188
7.3.1 Analog vs. digital backhaul 188
7.3.2 Joint base station and cloud processing with digital backhaul 191
7.3.3 Section summary 198
7.4 Big data aware wireless networking 198
7.4.1 Wireless big data analytics 200
7.4.2 Data-driven mobile cloud computing 205
7.4.3 Software-defined networking design 207
7.4.4 Section summary 209
7.5 Conclusions 210
Acknowledgement 211
References 211
8 Big data processing for smart grid security 217
Lanchao Liu, Zhu Han, H. Vincent Poor, and Shuguang Cui
8.1 Preliminaries and motivations 217
8.2 Sparse optimization for false data injection detection 219
8.2.1 State estimation and false data injection attacks 219
8.2.2 Nuclear norm minimization 223
8.2.3 Low-rank matrix factorization 225
8.2.4 Numerical results 227
8.3 Distributed approach for security-constrained optimal power flow 232
8.3.1 Security-constrained optimal power flow 232
8.3.2 ADMM method 235
8.3.3 Distributed and parallel approach for SCOPF 236
8.3.4 Numerical results 238
8.4 Concluding remarks 241
Acknowledgement 24
References 242
Part III Big data over social networks 245
9 Big data: a new perspective on cities 247
Riccardo Gallotti, Thomas Louail, Rémi Louf, and Marc Barthélémy
9.1 Big data and urban systems 247
9.2 Infrastructure networks
249
Contents
IX
9.2.1 Road networks 249
9.2.2 Subway networks 255
9.3 Mobility networks 257
9.3.1 A renewed interest 257
9.3.2 Individual mobility networks 258
9.3.3 From big data to the spatial structure of cities 261
9.4 Scaling in cities 268
9.5 Discussion: towards a new science of cities 272
Acknowledgments 273
References 273
10 High-dimensional network analytics: mapping topic networks in Twitter data
during the Arab Spring 278
Kathleen M. Carley, Wei Wei, and Kenneth Joseph
10.1 Introduction 278
10.2 Arab Spring 280
10.3 General background 280
10.4 Data 281
10.5 The social pulse: geo-temporal trends in Twitter topics and users 284
10.5.1 Methodology 284
10.5.2 Topic overview 285
10.5.3 Over time analysis 287
10.5.4 Characterization of user-topic similarity network 288
10.5.5 Social interaction overview: the reply network 290
10.5.6 Characterization of group structure 291
10.5.7 Key actors 294
10.6 Discussion 295
10.7 Conclusion 297
Acknowledgements 298
References 299
11 Social influence analysis in the big data era: a review 301
Jianping Cao, Dongliang Duan, Liuqing Yang, Qingpeng Zhang, Senzhang Wang, and Feiyue Wang
11.1 Introduction 301
11.2 Social influence measurement 304
11.2.1 Network-based measures 304
11.2.2 Behavior-based measures 309
11.2.3 Interaction-based measures 312
11.2.4 Topic-based measures 313
11.2.5 Other measures 316
11.3 Influence propagation and maximization 317
11.3.1 Opinion leader identification 317
11.3.2 Influence maximization 319
x Contents
11.3.3 Diffusion network inference
11.3.4 Challenges of IP M
11.4 Challenges in big data
11.5 Summary
Acknowledgement
References
Part IV Big data over biological networks
12 Inference of gene regulatory networks: validation and uncertainty
Xiaoning Qian, Byung-Jun Yoon, and Edward R. Dougherty
12.1 Introduction
12.2 Background
12.2.1 Markov chains
12.2.2 Logical regulatory networks
12.2.3 Control policy for maximal steady-state alteration
12.2.4 Inference algorithms
12.3 Network distance functions
12.3.1 Semi-metrics
12.3.2 Rule-based distance
12.3.3 Topology-based distance
12.3.4 Transition-probability-based distance
12.3.5 Steady-state distance
12.3.6 Control-based distance
12.4 Inference performance
12.4.1 Measuring inference performance using distance functions
12.4.2 Analytic example
12.4.3 Synthetic examples
12.5 Consistency
12.6 Approximation
12.7 Validation from experimental data
12.7.1 Metastatic melanoma network inference
12.8 Uncertainty quantification
12.8.1 Mean objective cost of uncertainty
12.8.2 Intervention in yeast cell cycle network with uncertainty
References
13 Inference of gene networks associated with the host response to
infectious disease
Zhe Gan, Xin Yuan, Ricardo Henao, Ephraim L. Tsalik, and Lawrence Carin
13.1 Background
13.2 Factor models in gene expression analysis
324
327
327
328
329
329
335
337
337
338
339
340
341
341
343
343
343
344
344
345
345
346
346
347
348
352
352
353
354
354
356
358
360
365
365
366
Contents
XI
13.3 Factor models 3 67
13.3.1 Shrinkage prior 368
13.3.2 Multiplicative gamma process 369
13.4 Discriminative models 370
13.4.1 Bayesian log-loss 370
13.4.2 Bayesian hinge-loss 372
13.5 Discriminative factor model 372
13.5.1 Multi-task learning 3 74
13.6 Inference 374
13.7 Experiments 376
13.7.1 Performance measures 377
13.7.2 Experimental setup 377
13.7.3 Classification results 378
13.7.4 Interpretation 3 82
13.8 Closing remarks 384
13.9 Inference details 385
Acknowledgements 387
References 388
14 Gene-set-based inference of biological network topologies from big molecular
profiling data 391
Lipi Acharya and Dongxiao Zhu
14.1 Introduction 391
14.2 Big data to network components 393
14.3 Gene sets related to network components 394
14.4 Reconstructing biological network topologies using gene sets 395
14.4.1 A general setting 395
14.4.2 Gene set Gibbs sampling 397
14.4.3 Gene set simulated annealing 397
14.5 Discussion and future work 403
References 406
15 Large-scale correlation mining for biomolecular network discovery 409
Alfred Hero and Bala Rajaratnam
15.1 Introduction 409
15.2 Illustrative example 414
15.2.1 Pairwise correlation 416
15.2.2 From pairwise correlation to networks of correlations 417
15.3 Principles of correlation mining for big data 419
15.3.1 Correlation mining for correlation flips between two
populations 424
15.3.2 Large-scale implementation of correlation mining 426
xii Contents
15.4 Perspectives and future challenges 427
15.4.1 State-of-the-art in correlation mining 427
15.4.2 Future challenges in correlation mining biomolecular networks 429
15.5 Conclusion 431
Acknowledgements 431
References 432
Index 437
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| spelling | Big data over networks edited by Shuguang Cui, Texas A&M University [und 3 weitere] First published Cambridge Cambridge University Press 2016 xx, 438 pages Diagramme 26 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Mathematik Big data Big data / Mathematics Computer networks Computer networks / Mathematics Nachweis (DE-588)4115334-0 gnd rswk-swf Netzwerk (DE-588)4171529-9 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Big Data (DE-588)4802620-7 s Netzwerk (DE-588)4171529-9 s Nachweis (DE-588)4115334-0 s DE-604 Cui, Shuguang (DE-588)1096827891 edt 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=029014115&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4115334-0 (DE-588)4171529-9 (DE-588)4802620-7 |
| title | Big data over networks |
| title_auth | Big data over networks |
| title_exact_search | Big data over networks |
| title_full | Big data over networks edited by Shuguang Cui, Texas A&M University [und 3 weitere] |
| title_fullStr | Big data over networks edited by Shuguang Cui, Texas A&M University [und 3 weitere] |
| title_full_unstemmed | Big data over networks edited by Shuguang Cui, Texas A&M University [und 3 weitere] |
| title_short | Big data over networks |
| title_sort | big data over networks |
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