Context-Aware Ranking with Factorization Models 9783642168970, 3642168973, 9783642168987, 3642168981
Context-aware ranking is an important task in search engine ranking. This book presents a generic method for context-awa
210 89 11MB
English Pages 180 [637] Year 2010;2011
Table of contents :
Cover......Page 1
Examples of Enabling Technologies of Information Integration and Surveillance......Page 3
Transaction Data Streams......Page 4
The Targets of Dataveillance......Page 6
Dataveillance and the Consumer: Business Intelligence......Page 8
Collective Behavior in Communities – Viral Media......Page 10
Introduction......Page 17
Related Work......Page 19
References......Page 21
Linda Coordination Frameworks......Page 22
Use Case Description......Page 24
Front Matter......Page 2
Uncertainty and Dataveillance......Page 11
Empowerment and Dataveillance and Technology Cooption......Page 12
Social Intelligence......Page 18
References......Page 23
Application of the Event-Model-F to Tourism and Sports......Page 25
Space-Based Computing......Page 26
Data Mashups......Page 5
A Social Ecological Model for Technology Cooption......Page 14
The Social Ecology Model and the Rise ICT Dataveillance......Page 15
Coordination Models......Page 20
The State and Dataveillance......Page 7
Life World Uses of Information and Dataveillance......Page 9
Dataveillance and Technology Cooption......Page 13
Part I Foundations and Principles......Page 16
Architecture and Integration......Page 27
References......Page 28
Hybrid Storage......Page 29
Development Environment......Page 30
Summary......Page 31
Applications......Page 32
Evaluation......Page 34
Conclusions......Page 39
References......Page 41
Java Message Service......Page 33
Linda Tuple Space......Page 35
Discussion......Page 37
Conclusion......Page 38
References......Page 40
Introduction......Page 45
Ant Colony Optimization State-of-the-Art......Page 46
Data Mining State-of-the-Art Elements......Page 48
Ant Colony Optimization......Page 50
Data Mining......Page 54
Classification......Page 55
Clustering......Page 56
Data Clustering and Ant Colony Optimization......Page 62
Applications and Examples......Page 65
Conclusions......Page 67
Key Terms......Page 69
References......Page 70
Introduction......Page 75
OpenSEA......Page 78
TOGAF – The Upper Ontology for OpenSEA......Page 79
ISO 24707:2007 – The Meta-ontology for OpenSEA......Page 80
OpenSEA – Developing an Upper Ontology with CL......Page 82
Developing an Upper Ontology from TOGAF......Page 84
Sowa’s Conceptual Catalogue......Page 85
Nested Graphs......Page 88
Contextualisation of Information......Page 89
OpenSEA and the Cloud......Page 92
OpenSEA and Web3.0......Page 94
Integration of OpenSEA and GoodRelations......Page 96
Conclusion......Page 97
www.Open-SEA.org......Page 98
Introduction......Page 101
From Folksonomies to Collective Tag Intelligence......Page 102
Tag Classification Approaches......Page 103
Similarity-Based Search Approaches......Page 104
Contributions of the Chapter......Page 105
Tag Equivalence Clusters......Page 106
Semantic Relations......Page 107
Terminological Relations......Page 109
Normalization Techniques......Page 110
Similarity-Based Techniques for Tag Matching......Page 113
Syntactic Similarity Function......Page 114
Semantic Similarity Function......Page 115
Terminological Similarity Function......Page 117
Linguistic Similarity Function......Page 118
Evaluation Issues......Page 119
An Example of Similarity-Based Resource Retrieval......Page 122
References......Page 124
Introduction......Page 127
State-of-the-Art: Distributed Decision-Making and Collaboration......Page 128
Computational Trust......Page 129
Computational Models of Trust......Page 131
Social Search......Page 133
Computational Trust to Enhance Social Search Ranking:A Practical Study-Case......Page 134
The Functioning......Page 136
Leveraging Users' Features to Rank Pages......Page 138
Performance Criteria......Page 139
Enhancement of Past Performance......Page 143
Experiment 1 - with Past Performance......Page 144
Experiment 2 - No Past Performance......Page 145
References......Page 147
Part II Advanced Models and Practices......Page 150
Introduction......Page 151
Formal Concept Analysis......Page 152
Formal Concept Analysis Scaling......Page 155
Formal Context Formats......Page 156
Large Data Sets......Page 158
Data Discretisation......Page 159
Data Booleanisation......Page 160
Concept and Lattice Generation......Page 163
Dealing with Concept Simplification and Complexity......Page 166
An Overall Process......Page 171
Semantic Web, RDF and OWL......Page 173
Conclusion......Page 175
References......Page 176
Constructing Ensemble Classifiers from GEP-Induced Expression Trees......Page 178
Introduction......Page 179
Using Gene Expression Programming to Induce Classifiers......Page 181
AdaBoost-Based Ensemble Classifier with GEP Learning (AB-GEP)......Page 186
AdaBoost-Based Ensemble Classifiers with Cellular GEP Learning (AB-cGEP)......Page 187
Majority-Voting-Based Ensemble Classifier with GEP Learning (MV-GEP and MVC-GEP)......Page 188
Majority-Voting-Based Ensemble Classifier with Cellular GEP Learning (MV-cGEP)......Page 190
Triplet Mass Function-Based Ensemble Classifier with GEP Learning (TMF-GEP)......Page 191
Computational Experiment Results......Page 195
Conclusions......Page 201
Introduction......Page 205
Related Approaches......Page 206
Basic SILBA......Page 209
Extended SILBA......Page 212
Swarm Based Algorithms......Page 213
Bee Algorithm......Page 215
Ant Algorithms......Page 218
Basic SILBA Benchmarks......Page 221
Extended SILBA Benchmarks......Page 222
Conclusion......Page 231
References......Page 232
Computational Intelligence in Future Wireless and Mobile Communications by Employing Channel Prediction Technology......Page 235
Introduction......Page 236
Direct Sequencing Spread Spectrum......Page 237
Frequency Hopping Spread Spectrum (FHSS)......Page 238
Channel Coding......Page 242
Representations for LDPC Codes......Page 243
Proposed Channel Prediction Scheme......Page 245
The Algorithm......Page 246
Spectrum Analysis of the Proposed MCFH-SS System......Page 247
Complementary Cumulative Distribution Function (CCDF)......Page 248
Adjacent Channel Power......Page 250
Interference Analysis of the Proposed MCFH-SS System......Page 252
Interference Analyzer......Page 254
Conclusions......Page 256
Introduction......Page 261
Motivations......Page 262
Definitions......Page 263
Contributions and Limitations......Page 264
Structure......Page 265
Cost of Time......Page 266
Strategic Points......Page 267
Participant Record......Page 271
Results......Page 272
Elements......Page 274
Conclusions and Future Work......Page 276
References......Page 277
Augmenting Human Intelligence in Goal Oriented Tasks......Page 280
Introduction......Page 281
Theoretical Foundation......Page 282
Knowledge Representation Ontology......Page 283
Knowledge Acquisition......Page 284
Agents......Page 286
ADD: AGUIA for Exploration of Options Guided by Models......Page 288
AGUIA for Script-Based Ontology Sensemaking......Page 291
AGUIA for Keeping User Awareness of Changes in Context......Page 294
AGUIA for Knowledge Amplification......Page 297
HYRIWYG: How You Rate Influences What You Get......Page 298
KA-CAPTCHA for Implicit Knowledge Acquisition......Page 301
Related Work......Page 303
Conclusion......Page 304
References......Page 306
Part III Advanced Applications......Page 309
Introduction......Page 310
Goal......Page 312
Challenges......Page 313
Other Applications......Page 316
Data Collection and Mining Using On-Demand Resources......Page 317
Overview......Page 318
Workflow......Page 321
Addressing the Challenges of Continuous MMOG Analytics......Page 322
CAMEO Implementation......Page 325
Cloud Infrastructure......Page 326
Experimental Results......Page 327
Understanding User Community Needs......Page 328
Enabling and Using Distributed and Collaborative Technology......Page 330
References......Page 332
Adaptive Fuzzy Inference Neural Network System for EEG and Stabilometry Signals Classification......Page 336
Introduction and Background......Page 337
EEG......Page 338
Stabilometry......Page 340
EEG......Page 341
Stabilometry......Page 343
Architecture of AFINN......Page 350
Evaluation of the Model Generation Method......Page 354
Evaluation of AFINN......Page 355
Discussion of Results......Page 356
Conclusions......Page 357
References......Page 358
Introduction......Page 363
Challenges and Current Position of SOA in Mobile World......Page 366
Components and Integrated Technology Stack for the Next Generation Mobility Architecture......Page 367
The Architecture Planning and Design......Page 368
Mobile SOA Implemented in University e-Learning Environment......Page 371
SOA-Based Service “Customer Search”......Page 372
“Applications – End-Devices” Connection Models......Page 374
First Scenario – Connecting via Servlets......Page 375
Second Scenario – SOA-Based......Page 376
Mathematical Model of Logical Architecture for the Mobile Connectivity Services......Page 377
Structural Design and Implementation......Page 378
General Definitions of Queuing Systems Used......Page 379
Conclusions......Page 384
References......Page 385
Introduction......Page 389
Our Contribution......Page 390
Board Based Games......Page 391
Theories on Entertainment......Page 393
Board Based Games......Page 394
Predator/Prey Games......Page 395
Board Based Games......Page 396
Predator/Prey Games......Page 397
Board Based Games......Page 398
Predator/Prey Games......Page 400
Software Agents......Page 402
Agent Using Min-Max with Rule Based Evaluation Function......Page 403
Agents for Predator/Prey Games......Page 404
Board Based Games......Page 406
Predator Prey Games......Page 411
References......Page 416
Leveraging Massive User Contributions for Knowledge Extraction......Page 420
Introduction......Page 421
Tag Clustering and Community Detection......Page 423
Temporal Tag Analysis......Page 424
Image Analysis Using Collaborative Data......Page 425
Tag Clustering through Community Detection in Tag Networks......Page 426
Description of MultiSCAN......Page 427
Evaluation of Tag Clustering......Page 429
The Proposed Framework......Page 432
Evaluation of Time-Aware User/Tag Co-clustering......Page 434
Enhancing Image Analysis Using Collaborative Data......Page 437
Framework Description......Page 438
Analysis Components......Page 439
Evaluation of Object Detection Models......Page 441
Conclusions......Page 444
References......Page 445
Introduction......Page 449
Motivation......Page 451
Related Work......Page 453
Problem Formalization......Page 454
Background Knowledge......Page 455
Characteristics of (k,,l)-Anonymity......Page 457
Satisfaction Algorithms......Page 460
Search by Slicing......Page 462
Data Sets......Page 466
Efficiency......Page 467
Space Complexity......Page 470
Conclusion and Future Work......Page 471
Part IV Future Trends and Concepts......Page 474
Introduction......Page 475
Decomposed NGN Architecture......Page 477
Advantages of New Technologies......Page 478
Applicability......Page 479
Methodology for Evaluation and Planning......Page 480
Network Intelligence Features......Page 481
Signaling Architecture......Page 483
Separation of Functions and Domains in NGN......Page 484
Service Domain Provides Open Software Platform......Page 485
Transport Domain Supplies Connectivity for Service Domain......Page 487
Distributed Processing Environment as Software Framework for Development and Deployment of Distributed Applications......Page 488
Next Generation Application Model......Page 489
Application Service-Enabling Platform......Page 490
NGN Service Features......Page 491
Typology of NGN Services......Page 492
Business Model for Service Engineering in NGN......Page 494
Modeling Methodology - Strategy, Processes, and Outcomes......Page 495
Challenges and Support in Business Decisions......Page 498
Significance of Applications Intelligence in NGN Business/Service Modeling and Engineering......Page 499
Conclusions and Implications for Future Work......Page 500
References......Page 502
Utilizing Next Generation Emerging Technologies for Enabling Collective Computational Intelligence in Disaster Management......Page 505
Introduction......Page 506
Grid Computing......Page 507
Pervasive Computing......Page 508
Crowd Sourcing......Page 509
Computational Collective Intelligence versus Collective Computational Intelligence: Are the Two the Same?......Page 510
Swarm Intelligence......Page 511
Multi-Agent Systems......Page 513
Motivation......Page 514
A Disaster Management Scenario......Page 515
Stakeholders’ Requirements......Page 516
Model Architecture......Page 518
A Technical Model Architecture......Page 519
Learning Birds for Information Gathering: A Future Implementation Scenario......Page 522
Conclusions......Page 523
References......Page 524
Emerging, Collective Intelligence for Personal, Organisational and Social Use......Page 529
Introduction......Page 530
Background......Page 532
Advances in Media Intelligence......Page 533
Advances in Organisational Intelligence......Page 534
Media Intelligence......Page 535
Community Detection on Tag Graphs......Page 541
Ontology-Based Classification......Page 544
Social Intelligence......Page 546
Protecting Virtual Communities......Page 547
The Community Design Language......Page 549
Event Log Merger Application......Page 550
Sharing Event Descriptions with SemaPlorer......Page 552
Application of the Event-Model-F to Tourism and Sports......Page 553
Collective Intelligence Methodology......Page 554
Architecture and Integration......Page 555
Hybrid Storage......Page 557
Development Environment......Page 558
Summary......Page 559
Applications......Page 560
Evaluation......Page 562
Conclusions......Page 567
References......Page 569
Introduction......Page 576
Mobile-Sensing of Collective Behavior in Organizations......Page 577
Sensing and Modeling Individual Behavior......Page 578
Sensing and Modeling Group Behavior......Page 579
Social Network Analysis......Page 580
Sensor-Based Organizational Design and Engineering......Page 581
Mobile Platforms for Collective Intelligence at the Community Scale......Page 584
Collective Behavior in Communities – Viral Media......Page 585
Collective Behavior in Communities – Political Opinions......Page 588
From Social Interaction Data to Collective Intelligence......Page 589
Collective Potential in Dynamic Networks......Page 591
Conclusions......Page 592
References......Page 595
Introduction......Page 599
Examples of Enabling Technologies of Information Integration and Surveillance......Page 601
Transaction Data Streams......Page 602
Measurement Data Streams......Page 603
The Targets of Dataveillance......Page 604
The State and Dataveillance......Page 605
Dataveillance and the Consumer: Business Intelligence......Page 606
Life World Uses of Information and Dataveillance......Page 607
Uncertainty and Dataveillance......Page 609
Empowerment and Dataveillance and Technology Cooption......Page 610
Dataveillance and Technology Cooption......Page 611
A Social Ecological Model for Technology Cooption......Page 612
The Social Ecology Model and the Rise ICT Dataveillance......Page 613
The Outcome of Technology Cooption: Surveillance Creep......Page 617
Conclusion......Page 618
References......Page 619
Back Matter......Page 624
Cover......Page 1
Examples of Enabling Technologies of Information Integration and Surveillance......Page 3
Transaction Data Streams......Page 4
The Targets of Dataveillance......Page 6
Dataveillance and the Consumer: Business Intelligence......Page 8
Collective Behavior in Communities – Viral Media......Page 10
Introduction......Page 17
Related Work......Page 19
References......Page 21
Linda Coordination Frameworks......Page 22
Use Case Description......Page 24
Front Matter......Page 2
Uncertainty and Dataveillance......Page 11
Empowerment and Dataveillance and Technology Cooption......Page 12
Social Intelligence......Page 18
References......Page 23
Application of the Event-Model-F to Tourism and Sports......Page 25
Space-Based Computing......Page 26
Data Mashups......Page 5
A Social Ecological Model for Technology Cooption......Page 14
The Social Ecology Model and the Rise ICT Dataveillance......Page 15
Coordination Models......Page 20
The State and Dataveillance......Page 7
Life World Uses of Information and Dataveillance......Page 9
Dataveillance and Technology Cooption......Page 13
Part I Foundations and Principles......Page 16
Architecture and Integration......Page 27
References......Page 28
Hybrid Storage......Page 29
Development Environment......Page 30
Summary......Page 31
Applications......Page 32
Evaluation......Page 34
Conclusions......Page 39
References......Page 41
Java Message Service......Page 33
Linda Tuple Space......Page 35
Discussion......Page 37
Conclusion......Page 38
References......Page 40
Introduction......Page 45
Ant Colony Optimization State-of-the-Art......Page 46
Data Mining State-of-the-Art Elements......Page 48
Ant Colony Optimization......Page 50
Data Mining......Page 54
Classification......Page 55
Clustering......Page 56
Data Clustering and Ant Colony Optimization......Page 62
Applications and Examples......Page 65
Conclusions......Page 67
Key Terms......Page 69
References......Page 70
Introduction......Page 75
OpenSEA......Page 78
TOGAF – The Upper Ontology for OpenSEA......Page 79
ISO 24707:2007 – The Meta-ontology for OpenSEA......Page 80
OpenSEA – Developing an Upper Ontology with CL......Page 82
Developing an Upper Ontology from TOGAF......Page 84
Sowa’s Conceptual Catalogue......Page 85
Nested Graphs......Page 88
Contextualisation of Information......Page 89
OpenSEA and the Cloud......Page 92
OpenSEA and Web3.0......Page 94
Integration of OpenSEA and GoodRelations......Page 96
Conclusion......Page 97
www.Open-SEA.org......Page 98
Introduction......Page 101
From Folksonomies to Collective Tag Intelligence......Page 102
Tag Classification Approaches......Page 103
Similarity-Based Search Approaches......Page 104
Contributions of the Chapter......Page 105
Tag Equivalence Clusters......Page 106
Semantic Relations......Page 107
Terminological Relations......Page 109
Normalization Techniques......Page 110
Similarity-Based Techniques for Tag Matching......Page 113
Syntactic Similarity Function......Page 114
Semantic Similarity Function......Page 115
Terminological Similarity Function......Page 117
Linguistic Similarity Function......Page 118
Evaluation Issues......Page 119
An Example of Similarity-Based Resource Retrieval......Page 122
References......Page 124
Introduction......Page 127
State-of-the-Art: Distributed Decision-Making and Collaboration......Page 128
Computational Trust......Page 129
Computational Models of Trust......Page 131
Social Search......Page 133
Computational Trust to Enhance Social Search Ranking:A Practical Study-Case......Page 134
The Functioning......Page 136
Leveraging Users' Features to Rank Pages......Page 138
Performance Criteria......Page 139
Enhancement of Past Performance......Page 143
Experiment 1 - with Past Performance......Page 144
Experiment 2 - No Past Performance......Page 145
References......Page 147
Part II Advanced Models and Practices......Page 150
Introduction......Page 151
Formal Concept Analysis......Page 152
Formal Concept Analysis Scaling......Page 155
Formal Context Formats......Page 156
Large Data Sets......Page 158
Data Discretisation......Page 159
Data Booleanisation......Page 160
Concept and Lattice Generation......Page 163
Dealing with Concept Simplification and Complexity......Page 166
An Overall Process......Page 171
Semantic Web, RDF and OWL......Page 173
Conclusion......Page 175
References......Page 176
Constructing Ensemble Classifiers from GEP-Induced Expression Trees......Page 178
Introduction......Page 179
Using Gene Expression Programming to Induce Classifiers......Page 181
AdaBoost-Based Ensemble Classifier with GEP Learning (AB-GEP)......Page 186
AdaBoost-Based Ensemble Classifiers with Cellular GEP Learning (AB-cGEP)......Page 187
Majority-Voting-Based Ensemble Classifier with GEP Learning (MV-GEP and MVC-GEP)......Page 188
Majority-Voting-Based Ensemble Classifier with Cellular GEP Learning (MV-cGEP)......Page 190
Triplet Mass Function-Based Ensemble Classifier with GEP Learning (TMF-GEP)......Page 191
Computational Experiment Results......Page 195
Conclusions......Page 201
Introduction......Page 205
Related Approaches......Page 206
Basic SILBA......Page 209
Extended SILBA......Page 212
Swarm Based Algorithms......Page 213
Bee Algorithm......Page 215
Ant Algorithms......Page 218
Basic SILBA Benchmarks......Page 221
Extended SILBA Benchmarks......Page 222
Conclusion......Page 231
References......Page 232
Computational Intelligence in Future Wireless and Mobile Communications by Employing Channel Prediction Technology......Page 235
Introduction......Page 236
Direct Sequencing Spread Spectrum......Page 237
Frequency Hopping Spread Spectrum (FHSS)......Page 238
Channel Coding......Page 242
Representations for LDPC Codes......Page 243
Proposed Channel Prediction Scheme......Page 245
The Algorithm......Page 246
Spectrum Analysis of the Proposed MCFH-SS System......Page 247
Complementary Cumulative Distribution Function (CCDF)......Page 248
Adjacent Channel Power......Page 250
Interference Analysis of the Proposed MCFH-SS System......Page 252
Interference Analyzer......Page 254
Conclusions......Page 256
Introduction......Page 261
Motivations......Page 262
Definitions......Page 263
Contributions and Limitations......Page 264
Structure......Page 265
Cost of Time......Page 266
Strategic Points......Page 267
Participant Record......Page 271
Results......Page 272
Elements......Page 274
Conclusions and Future Work......Page 276
References......Page 277
Augmenting Human Intelligence in Goal Oriented Tasks......Page 280
Introduction......Page 281
Theoretical Foundation......Page 282
Knowledge Representation Ontology......Page 283
Knowledge Acquisition......Page 284
Agents......Page 286
ADD: AGUIA for Exploration of Options Guided by Models......Page 288
AGUIA for Script-Based Ontology Sensemaking......Page 291
AGUIA for Keeping User Awareness of Changes in Context......Page 294
AGUIA for Knowledge Amplification......Page 297
HYRIWYG: How You Rate Influences What You Get......Page 298
KA-CAPTCHA for Implicit Knowledge Acquisition......Page 301
Related Work......Page 303
Conclusion......Page 304
References......Page 306
Part III Advanced Applications......Page 309
Introduction......Page 310
Goal......Page 312
Challenges......Page 313
Other Applications......Page 316
Data Collection and Mining Using On-Demand Resources......Page 317
Overview......Page 318
Workflow......Page 321
Addressing the Challenges of Continuous MMOG Analytics......Page 322
CAMEO Implementation......Page 325
Cloud Infrastructure......Page 326
Experimental Results......Page 327
Understanding User Community Needs......Page 328
Enabling and Using Distributed and Collaborative Technology......Page 330
References......Page 332
Adaptive Fuzzy Inference Neural Network System for EEG and Stabilometry Signals Classification......Page 336
Introduction and Background......Page 337
EEG......Page 338
Stabilometry......Page 340
EEG......Page 341
Stabilometry......Page 343
Architecture of AFINN......Page 350
Evaluation of the Model Generation Method......Page 354
Evaluation of AFINN......Page 355
Discussion of Results......Page 356
Conclusions......Page 357
References......Page 358
Introduction......Page 363
Challenges and Current Position of SOA in Mobile World......Page 366
Components and Integrated Technology Stack for the Next Generation Mobility Architecture......Page 367
The Architecture Planning and Design......Page 368
Mobile SOA Implemented in University e-Learning Environment......Page 371
SOA-Based Service “Customer Search”......Page 372
“Applications – End-Devices” Connection Models......Page 374
First Scenario – Connecting via Servlets......Page 375
Second Scenario – SOA-Based......Page 376
Mathematical Model of Logical Architecture for the Mobile Connectivity Services......Page 377
Structural Design and Implementation......Page 378
General Definitions of Queuing Systems Used......Page 379
Conclusions......Page 384
References......Page 385
Introduction......Page 389
Our Contribution......Page 390
Board Based Games......Page 391
Theories on Entertainment......Page 393
Board Based Games......Page 394
Predator/Prey Games......Page 395
Board Based Games......Page 396
Predator/Prey Games......Page 397
Board Based Games......Page 398
Predator/Prey Games......Page 400
Software Agents......Page 402
Agent Using Min-Max with Rule Based Evaluation Function......Page 403
Agents for Predator/Prey Games......Page 404
Board Based Games......Page 406
Predator Prey Games......Page 411
References......Page 416
Leveraging Massive User Contributions for Knowledge Extraction......Page 420
Introduction......Page 421
Tag Clustering and Community Detection......Page 423
Temporal Tag Analysis......Page 424
Image Analysis Using Collaborative Data......Page 425
Tag Clustering through Community Detection in Tag Networks......Page 426
Description of MultiSCAN......Page 427
Evaluation of Tag Clustering......Page 429
The Proposed Framework......Page 432
Evaluation of Time-Aware User/Tag Co-clustering......Page 434
Enhancing Image Analysis Using Collaborative Data......Page 437
Framework Description......Page 438
Analysis Components......Page 439
Evaluation of Object Detection Models......Page 441
Conclusions......Page 444
References......Page 445
Introduction......Page 449
Motivation......Page 451
Related Work......Page 453
Problem Formalization......Page 454
Background Knowledge......Page 455
Characteristics of (k,,l)-Anonymity......Page 457
Satisfaction Algorithms......Page 460
Search by Slicing......Page 462
Data Sets......Page 466
Efficiency......Page 467
Space Complexity......Page 470
Conclusion and Future Work......Page 471
Part IV Future Trends and Concepts......Page 474
Introduction......Page 475
Decomposed NGN Architecture......Page 477
Advantages of New Technologies......Page 478
Applicability......Page 479
Methodology for Evaluation and Planning......Page 480
Network Intelligence Features......Page 481
Signaling Architecture......Page 483
Separation of Functions and Domains in NGN......Page 484
Service Domain Provides Open Software Platform......Page 485
Transport Domain Supplies Connectivity for Service Domain......Page 487
Distributed Processing Environment as Software Framework for Development and Deployment of Distributed Applications......Page 488
Next Generation Application Model......Page 489
Application Service-Enabling Platform......Page 490
NGN Service Features......Page 491
Typology of NGN Services......Page 492
Business Model for Service Engineering in NGN......Page 494
Modeling Methodology - Strategy, Processes, and Outcomes......Page 495
Challenges and Support in Business Decisions......Page 498
Significance of Applications Intelligence in NGN Business/Service Modeling and Engineering......Page 499
Conclusions and Implications for Future Work......Page 500
References......Page 502
Utilizing Next Generation Emerging Technologies for Enabling Collective Computational Intelligence in Disaster Management......Page 505
Introduction......Page 506
Grid Computing......Page 507
Pervasive Computing......Page 508
Crowd Sourcing......Page 509
Computational Collective Intelligence versus Collective Computational Intelligence: Are the Two the Same?......Page 510
Swarm Intelligence......Page 511
Multi-Agent Systems......Page 513
Motivation......Page 514
A Disaster Management Scenario......Page 515
Stakeholders’ Requirements......Page 516
Model Architecture......Page 518
A Technical Model Architecture......Page 519
Learning Birds for Information Gathering: A Future Implementation Scenario......Page 522
Conclusions......Page 523
References......Page 524
Emerging, Collective Intelligence for Personal, Organisational and Social Use......Page 529
Introduction......Page 530
Background......Page 532
Advances in Media Intelligence......Page 533
Advances in Organisational Intelligence......Page 534
Media Intelligence......Page 535
Community Detection on Tag Graphs......Page 541
Ontology-Based Classification......Page 544
Social Intelligence......Page 546
Protecting Virtual Communities......Page 547
The Community Design Language......Page 549
Event Log Merger Application......Page 550
Sharing Event Descriptions with SemaPlorer......Page 552
Application of the Event-Model-F to Tourism and Sports......Page 553
Collective Intelligence Methodology......Page 554
Architecture and Integration......Page 555
Hybrid Storage......Page 557
Development Environment......Page 558
Summary......Page 559
Applications......Page 560
Evaluation......Page 562
Conclusions......Page 567
References......Page 569
Introduction......Page 576
Mobile-Sensing of Collective Behavior in Organizations......Page 577
Sensing and Modeling Individual Behavior......Page 578
Sensing and Modeling Group Behavior......Page 579
Social Network Analysis......Page 580
Sensor-Based Organizational Design and Engineering......Page 581
Mobile Platforms for Collective Intelligence at the Community Scale......Page 584
Collective Behavior in Communities – Viral Media......Page 585
Collective Behavior in Communities – Political Opinions......Page 588
From Social Interaction Data to Collective Intelligence......Page 589
Collective Potential in Dynamic Networks......Page 591
Conclusions......Page 592
References......Page 595
Introduction......Page 599
Examples of Enabling Technologies of Information Integration and Surveillance......Page 601
Transaction Data Streams......Page 602
Measurement Data Streams......Page 603
The Targets of Dataveillance......Page 604
The State and Dataveillance......Page 605
Dataveillance and the Consumer: Business Intelligence......Page 606
Life World Uses of Information and Dataveillance......Page 607
Uncertainty and Dataveillance......Page 609
Empowerment and Dataveillance and Technology Cooption......Page 610
Dataveillance and Technology Cooption......Page 611
A Social Ecological Model for Technology Cooption......Page 612
The Social Ecology Model and the Rise ICT Dataveillance......Page 613
The Outcome of Technology Cooption: Surveillance Creep......Page 617
Conclusion......Page 618
References......Page 619
Back Matter......Page 624
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