دانلود کتاب Digital Business: Business Algorithms, Cloud Computing and Data Engineering

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Preface......Page 6
Editorial......Page 8
Contents......Page 12
About the Editors......Page 14
Digital Business Transformation......Page 17
Towards Algorithmic Business: A Paradigm Shift in Digital Business......Page 18
1 Introduction......Page 19
2 Emergence of Algorithms in Business......Page 21
3 Digital to Algorithmic Transformation......Page 23
4 Deployment Strategies of IoT......Page 24
5.1 Google’s Algorithms......Page 27
5.2 Amazon’s Algorithm......Page 29
5.3 Face Book’s News Feed Algorithm......Page 30
6.1 Manufacturing Industries......Page 31
6.4 Retail and E-Commerce Industries......Page 32
6.8 Human Resources......Page 33
7 Challenges......Page 34
References......Page 35
A Decentralized Business Ecosystem Model for Complex Products......Page 38
1 Introduction......Page 39
2 Background and Methodology......Page 40
2.1 Business Ecosystems......Page 41
2.2 Methodology......Page 42
3.1 Complex Product Scenarios......Page 43
4 Related Work......Page 46
5.1 Model and Concept......Page 50
5.2 Ecosystem Structure......Page 52
6.1 High-Level Overview......Page 55
6.2 Peer Application......Page 56
6.3 Decentralized Market Space (DMS)......Page 60
6.5 Demonstrator......Page 62
7 Conclusions and Outlook......Page 65
References......Page 66
Compliance Management in Business Processes......Page 68
1 Introduction or Overview......Page 69
2 Compliance Management—Concepts and Terminology......Page 71
3 Case Study......Page 73
4.1 Elicitation......Page 76
4.2 Formalization (Specification)......Page 86
4.3 Implementation......Page 90
4.4 Supervision......Page 93
4.5 Improvement......Page 94
5 Compliance Monitoring Functionalities (CMFs)......Page 96
6 Challenges......Page 98
7 Conclusion......Page 100
References......Page 101
Cloud Computing......Page 107
1 Introduction......Page 108
2 Sustainable Cloud Computing: Architecture......Page 109
3 State-of-the-Art: Application Management......Page 111
4.1 QoS Parameter......Page 114
4.2 Application Parameters......Page 116
4.4 Application Category......Page 118
4.6 Resource Administration......Page 121
4.8 Application Model......Page 122
5.1 Application Modeling......Page 123
5.4 Reliability and Fault Tolerance......Page 124
5.7 Latency......Page 125
5.10 New Technological Developments......Page 126
References......Page 128
Auction Based Scheme for Resource Allotment in Cloud Computing......Page 131
2 Related Work......Page 132
2.1 A Taxonomy of Market Technical Aspects......Page 134
2.2 Auction Theory......Page 135
3 Market-Based Resource Management Model......Page 139
4.1 Architecture of Cloud......Page 141
4.3 Simulation Parameters......Page 146
5 Result and Discussion......Page 149
6 Conclusion......Page 150
References......Page 151
1 Introduction......Page 154
2.2 Weather Information......Page 156
3 Cloud Computing......Page 157
4 Agriculture with M-Cloud Computing......Page 158
4.2 Smarter Agricultural Practices......Page 160
5 M-Cloud Computing with Big Data......Page 161
7 Conclusion......Page 163
References......Page 164
IOT & Mobility......Page 165
1 Introduction......Page 166
2 Related Work......Page 167
3 Proposed Work......Page 168
3.1 Databases......Page 169
3.2 Drowsiness Detection......Page 171
3.3 EEG Signal Classification......Page 173
3.4 SVM Classifier......Page 175
3.5 Yawning Detection......Page 176
4.2 Using Video Signals......Page 177
5 Conclusion......Page 181
References......Page 182
Enhancing Security and Privacy in Enterprises Network by Using Biometrics Technologies......Page 184
1 Introduction......Page 185
3 Interest of Business Networks......Page 186
3.2 Sensitive Business Information......Page 187
4.1 Cryptography Process......Page 188
5 Crypto-Biometric Based EN Security......Page 189
6.1 Preliminary......Page 192
6.2 Encryption and Key Embedded Process......Page 194
6.3 Decryption and Key Retrieval Process......Page 195
7 Experimental Setup......Page 197
8.1 Unimodal Biometric Cryptosystem......Page 200
8.2 Multimodal Biometric Cryptosystem......Page 203
9 Conclusion and Further Works......Page 204
References......Page 205
Lightweight Context-Based Web-Service Composition Model for Mobile Devices......Page 207
1 Introduction......Page 208
3.1 Parts of Speech (POS) Tagging......Page 209
3.2 Markov Chain Probabilistic Model......Page 215
3.3 Trie Data Structure......Page 216
4 Methodology and Implementation......Page 217
4.1 Get Natural Language Query from the User......Page 218
4.3 Framing Sample Sentences......Page 219
4.4 Using Markov Chain Probabilistic Model......Page 220
4.6 API’s and Libraries Used in Web Service......Page 222
5 Results and Discussion......Page 227
6 Conclusion and Future Work......Page 228
References......Page 229
1 Introduction......Page 231
2 WECREn and Its Dynamic Form......Page 235
3 Characterization Results......Page 241
4 Empirical Approach......Page 245
References......Page 248
Impact of Mobility in IoT Devices for Healthcare......Page 250
1.2 Extent amp Objectives of the Research......Page 251
1.3 Significance of the Research......Page 252
1.4 Applications of the Research......Page 253
2 Literature Survey......Page 254
3 Problem Statement for Impacts of Mobility in IoT Devices for Healthcare......Page 255
3.1 Difficulties Which Impact of Mobility in IOT Devices for Healthcare Sector......Page 256
3.3 Disadvantages of Existing System......Page 257
3.5 Advantages of Proposed System......Page 258
4 System Security Standards......Page 259
5 Security Healthcare Challenges......Page 260
6 Mobility Issues......Page 261
8 Performance Analysis......Page 262
9 Disadvantages in Traditional Method......Page 263
10 Conclusion......Page 266
Bibliography......Page 267
Multiple Mobile Elements Based Energy Efficient Data Gathering Technique in Wireless Sensor Networks......Page 269
1 Introduction......Page 270
2 Related Work......Page 271
3 Practical Understanding of Unresolved Issues......Page 273
3.1 Motivation......Page 274
4.1 Overview of the Proposed Work EEDGMME......Page 275
4.2 Detailed Explanation of Inclusive Algorithms......Page 276
5.2 Performance Metrics......Page 284
5.3 Performance Analysis of EEDGMME and EEDG Techniques......Page 285
5.4 Comparative Performance Analysis......Page 287
References......Page 289
Information Management & Social Media......Page 292
Online Social Communities......Page 293
1 Introduction......Page 294
2 Types of Social Communities......Page 296
3 Evolution of Technology for ONSC......Page 298
4 Online Social Community......Page 299
4.1 Pre-requisite for ONSC......Page 300
4.2 Types of ONSC......Page 306
5 Online Social Community Management......Page 308
5.2 User Engagement Factor......Page 310
5.3 User Retention Factors......Page 311
5.4 ONSC and User’s Perspective......Page 314
5.6 ONSC User Engagement Tools......Page 319
6 ONSC Structure Detection......Page 321
6.1 Detection of Communities in Static Networks......Page 322
6.2 Detection of Communities in Dynamic Networks......Page 328
7 Applications......Page 330
8 Conclusion......Page 332
References......Page 337
The ‘Verticals’, ‘Horizontals’, and ‘Diagonals’ in Organisational Communication: Developing Models to Mitigate Communication Barriers Through Social Media Applications......Page 346
1 Introduction......Page 347
2 Review of Literature......Page 348
2.1 History of Communication Skills......Page 349
2.2 New Capitalism: Services Sector and Linguistic Requirements......Page 350
2.3 Rhetorics of Communication and Workplace Demands......Page 351
3.1 The Model of Communication......Page 352
3.2 Communication Flows......Page 353
3.3 Barriers to Communication......Page 354
4 New Technologies for Communication......Page 357
4.1 Technologisation of Communication......Page 358
4.2 Capitalising Social Tools as an Organisational Discourse......Page 360
5 Social Tools......Page 361
6 Conclusion: Scope, Limitations, and Future Directions......Page 373
References......Page 374
1 Introduction......Page 377
2.1 Association Rule Mining......Page 379
3 Interestingness......Page 381
3.1 Objective Interestingness......Page 382
3.2 Subjective Interestingness......Page 383
4 Classification Framework for Subjective Interestingness......Page 385
5 Discussion and Conclusions......Page 387
References......Page 389
1 Introduction......Page 392
2 Related Work......Page 394
3.2 Random Mapping: Random Kitchen Sink Method......Page 395
3.3 Regularized Least Square Method for Classification (RLSC)......Page 397
3.4 Deep Learning Methods—CNN, LSTM......Page 398
4 Experimental Setup......Page 400
4.2 Preprocessing......Page 401
4.3 Models......Page 402
4.4 Evaluation......Page 403
5 Result and Analysis......Page 404
References......Page 407
1 Introduction......Page 410
2 Methodology Followed......Page 411
3 Tweet Collection......Page 412
4 Data Preprocessing......Page 413
6 Sentiment Analysis......Page 414
7 Location Based Analysis......Page 416
7.1 Region Wise Analysis......Page 418
7.2 State Wise Analysis......Page 419
8 Possible Reason of Unhappiness Among Various States......Page 421
9 Conclusions......Page 425
References......Page 428
Event Detection Using Twitter Platform......Page 429
1 Introduction......Page 430
2 Types of Online Social Networks......Page 433
3 Event Categories......Page 435
4 Event Detection......Page 438
4.1 Event Detection Techniques......Page 439
4.2 Research Challenges......Page 451
5 Case Study: Twitter Platform......Page 452
5.1 Twitter Platform for Detection of Disaster Events......Page 455
5.2 Twitter Platform for Detection of Emerging Events......Page 457
5.3 Twitter Platform for Public Opinion Events......Page 459
5.4 Twitter Platform for Prediction of Emerging Events......Page 460
6.1 Data Collection Tools......Page 461
6.2 Event Detection and Analysis Tools......Page 466
7 Conclusion......Page 471
References......Page 472