Supporting Real Time Decision-Making (eBook)

Frada Burstein completed her PhD at the Institute of Cybernetics, Georgian Academy of Science (the Branch of USSR Academy of Sciences) in 1981. She has an extensive research record in the areas of decision theory and decision support systems. Since 1992 she has been working in the Department of Information Systems of the Faculty of Information Technology at Monash University, and is currently an Associate Professor, Associate Dean for Research Training, Founder and Director of the Knowledge Management Research Program and a virtual laboratory at the Caulfield School of IT. Dr. Burstein was co-editor of Handbook of Decision Support Systems (with Clyde Holsapple) published by Springer in 2008. She has been Program Co-Chair for the Australian Conference on Knowledge Management and Intelligent Decision Support, Steering Committee member and Doctoral Colloquium faculty for the IFIP TC8/WG8.3 Working Conference International Conference on Collaborative Decision Making, Executive Committee Member of the Australian Council of Professors & Heads of Information Systems, Chair for the Association of Information Systems Special Interest Group in DSS, and Secretary for the IFIP WG 8.3 Decision Support Systems. Dr. Burstein’s many editorial board assignments include serving as Guest Co-Editor for the special issue of Decision Support Systems "Decision Support in an Uncertain and Complex World" (with GeorgeWidmeyer), Guest Co-Editor for the special issues of the journal Information Systems and E-Business Management (with Clyde Holsapple), Area Editor for Decision Support Systems, Associate Editor for Journal of Decision Systems, Associate Editor for International Journal of Knowledge Management, and editorial board member for Journal of Knowledge Management Research and Practice and Journal of Information and Knowledge Management. In 2003, Dr. Burstein received the Monash University Postgraduate Association Supervisor of the Year award.Patrick Brezillon is a researcher at the National Center for Scientific Research in Paris where his work focuses on the study of intelligent assistant systems, particularly the modelling of context and the relationship between collaborative decision making and context. His papers have appeared in such journals as IEEE Intelligent Systems, Knowledge Engineering Review, Expert Systems with Applications, International Journal on Human-Computer Studies, and AI Magazine. Dr. Brezillon directs the SART project, which is developing a system to support subway traffic control decisions when incidents occur, and the PROTEUS program, which is developing an integrated platform for supporting e-maintenance strategy. He was co-editor of Creativity and Innovation in Decision Making and Decision Support, Volumes 1 and 2, Ludic Publishing Ltd.List of publications: http://www-poleia.lip6.fr/~brezil/Pages2/Publications/index.htmlArkady Zaslavsky is a Chaired Professor at the Luleå University of Technology, Sweden and Associate Professor at Monash University, Australia. He received MSc in Applied Mathematics majoring in Computer Science from Tbilisi State University (Georgia, USSR) and Ph.D. in Computer Science from the Moscow Institute for Control Sciences, USSR Academy of Sciences. His research interests include mobile and pervasive computing, context-awareness, distributed and mobile agents and objects, wireless networks, distributed computing and database systems, distributed object technology and mobile commerce. His research is published in such journals as Telecommunication Systems, Mobile Networks and Applications, Journal of Organizational Computing and Electronic Commerce, Electronic Commerce Research, and ACM SIGMOD Record, IEEE Transactions on Systems, Man, Cybernetics, Pervasive and Mobile Computing. He has edited four books of conference proceedings published in LNCS series. List of publications: http://www.ltu.se/forskning/1.16009?pureId=1161&l=en&pureFamily=dk.atira.pure.families.person.shared.model.Person&sortOrder=desc

Foreword 6
Preface 8
Acknowledgements 14
Contents 16
Contributors 18
Author Biographies 24
Part I Theories of Real-Time Decision Support 42
Chapter 1: Challenges of Real-Time Decision Support 43
1 Introduction 43
2 Defining Real-time Decision Support 44
3 Operational and Tactical Decision Support 45
4 Challenges 47
4.1 Technical Challenges 48
4.2 Organisational Challenges 48
4.3 Social/Psychological Challenges 48
5 Conclusions 49
References 50
Chapter 2: Improvisation as Model for Real-Time Decision Making 52
1 Introduction and Research Objectives 52
2 Real-Time Dynamic Decision Making Contexts and the Relationship with Improvisation 53
3 Towards a Shared Understanding of Improvisation 55
3.1 History of Interest in Improvisation 55
3.2 Defining Improvisation 56
3.3 A Typology of Improvisational Contexts 58
3.4 Antecedents of Improvisation 60
3.5 Degrees of Improvisation 61
3.6 Performance and the Episodic Nature of Improvisation 62
3.7 Elements Necessary for Individual and Team Improvisation 63
3.8 Improvising Effectively 63
4 Implications for Real-Time Dynamic Decision Support Systems (DSS) 65
4.1 Pre-performance Support 66
4.2 Performance Support 67
4.3 Post-performance Support 67
4.4 Systems and Organisational Implications 68
5 Summary and Conclusions 69
References 70
Part II Tools and Technologies for Context-awareReal-Time Decision Support 72
Chapter 3: Context Prediction in Pervasive Computing Systems: Achievements and Challenges 73
1 Context and Context Prediction 73
2 Context Prediction Task 74
2.1 Context Prediction Task 74
2.2 From Task Definition to Evaluation Criteria 76
3 Context Prediction Methods 80
3.1 Sequence Prediction Approach 81
3.2 Markov Chains for Context Prediction 83
3.3 Neural Networks for Context Prediction 87
3.4 Bayesian Networks for Context Prediction 88
3.5 Branch Prediction Methods for Context Prediction 90
3.6 Trajectory Prolongation Approach for Context Prediction 91
3.7 Expert Systems for Context Prediction 91
3.8 Context Prediction Approaches Summary 93
4 General Approaches to Context Prediction 93
5 Research Challenges of Context Prediction 97
References 98
Chapter 4: A Contextual Methodology for Modelling Real-Time Decision-Making Support 102
1 Introduction 102
2 Background of the Contextual Methodology 104
2.1 Modelling of Procedure and Practice 104
2.2 A Context Representation by Contextual Elements 105
2.3 The Four-Level Representation of Human Reasoning 106
2.3.1 Policy Level 107
2.3.2 Strategic Level 108
2.3.3 Tactical Level 108
2.3.4 Operational Level 108
2.3.5 Summing Up 109
2.4 A Three-Layer Model 109
3 The Contextual Methodology 111
3.1 The 10 Steps 111
3.2 Application in the Modelling of Drivers’ Behaviour 114
4 Conclusion 123
References 124
Chapter 5: Towards Real-Time Context Awareness for Mobile Users: A Declarative Meta-Programming Approach 126
1 Introduction 126
2 A Brief Introduction to LogicCAP 128
2.1 LogicCAP Situation Programs 129
2.2 LogicCAP Prolog: Meta-Programming with Situation Programs 130
3 Prototype Implementation 132
3.1 Architecture of LogicCAP-S with the Context Toolkit 132
3.2 Extension 1: Extending the Vanila Metainterpreter in LogicCAP-S with Concurrent Event-Driven Behaviour 134
3.3 Extension 2: Levels of Abstraction, Dynamic Situation Program Binding, Dynamic Sensor Binding, and Goal Evaluations in the Mobile Environment 138
3.4 Extension 3: Control of Goal Evaluation – Persistent Queries, Forced Contraction, and Partially Complete Evaluations 141
4 Modes of Usage in Mobile Environments 142
4.1 Situation Programs As Mobile Context (and Sensor) Mashups 142
5 Related Work 145
6 Conclusion and Future Work 146
References 147
Chapter 6: The Role of Context for Crisis Management Cycle 150
1 Introduction 151
2 Need of Context for Crisis Management 152
3 Crisis Management 154
3.1 Crisis Characteristics 154
3.2 From Crisis Management Concept to Crisis Management Support 156
4 Contextualisation Use in the Three Phases of DICE Management 159
4.1 Contextualisation for Information Gathering 159
4.2 Contextualisation to Get a Meaningful Situation Awareness 162
4.3 Dual Impact of the Context on Decision Making 163
4.4 Contextualisation of the DICE Management Upgrade Process 165
5 Conclusion 166
References 167
Chapter 7: The Contextual and Collaborative Dimensions of Avatar in Real-Time Decision Making 170
1 Introduction 170
2 Background 172
2.1 Second Life 172
2.2 Second Life as an Environment for real-timeDecision Making 174
3 Research Model 177
4 Research Methodology 178
4.1 Subjects 179
5 Results 180
6 Discussion and Implications 181
6.1 Implications for Research 182
6.2 Implication for Practice 182
7 Conclusions and Future Work 183
7.1 Future Work 184
8 Appendix I: Example of Avatars 188
9 Appendix II: Survey Questions 189
10 Appendix III: General Linear Model 191
Part III Case Studies 194
Chapter 8: Beyond Rationality: Information Design for Supporting Emergent Groups in Emergency Response 195
1 Introduction 195
2 Emergency Assistance, Emergent Groups and Context-Based Decision Support 197
3 Theoretical Perspective on Information Requirements for Emergent Group Support 198
3.1 Template as Means of Information Support to Emergent Groups 200
3.2 Problem-Solving Approach 201
3.3 Action-Resource-Based Approach to Defining Information Needs 203
3.4 Control and Monitoring Approach to Managing Complex Organisations 204
3.5 Motivational Approach Based on the Altruistic Community Model 205
3.6 Sense Making and an Affective Approach to Deal with Disaster-Induced Stress 208
4 Conclusion 211
References 212
Chapter 9: Dynamic Emergency Response Management for Large Scale Decision Making in Extreme Hazardous Events 216
1 Introduction 216
2 The Nature of Emergencies 217
2.1 Large Groups and Subgroups of Decision-Analysis Teams 223
3 Formulating Group Viewpoints 223
4 Theory and Use of Emergency Management Systems 227
4.1 Groups as High Reliability Organisations 227
4.2 Building Knowledge, Experts Voting 228
5 Other Design Problems for Emergency Decision Support Systems 229
6 Threat Rigidity Syndrome 229
7 Information Overload in Emergency Management 231
8 High Reliability Organisations and Muddling Through 232
9 Observations and Conclusions 234
References 235
Chapter 10: Partially Distributed Emergency Teams: Considerations of Decision Support for Virtual Communities of Practice 238
1 Introduction 238
2 Partially Distributed Emergency Teams 239
2.1 Trust and PDETs 240
2.2 Role Management to Support PDETs 241
3 Training Members of PDETs 244
3.1 Wikis 244
3.2 Gaming 244
3.3 Collaborative Scenario and Exercise Generation 245
4 The Gold Mine: Citizen Participation 246
4.1 Warning System Development 246
4.2 Social Media and Citizen Participation 248
4.3 Global Community Participation 249
5 Conclusion 251
References 254
Chapter 11: Why Real-Time Transaction Processing Fails to Capture the Context Required for Decision Support 256
1 Introduction 256
2 A Context-based Representation of the Decision Maker 257
3 Research Methodology 258
4 The Case of the Deliver Process at SIT 260
4.1 Context and Justification in Selecting This Case Study 260
4.2 Information Systems Supporting the Process 262
5 Analysing the Case Study 262
5.1 Decision Making Remains Manual 262
5.2 Constraints Arising from the Physical Business Context 263
5.3 Constraints Arising from the Virtual Context (System Related) 265
5.4 Decoupling the ERP System 267
6 Discussion 268
7 Conclusions and Guidelines for Technological Solutions 270
References 271
Chapter 12: Continuous Auditing as a Foundation for Real Time Decision Support: Implementation Challenges and Successes 272
1 Introduction 272
2 Definition 274
3 Architecture 274
3.1 Digitised Data Source 275
3.2 Data Validation Engine 276
4 Implementation Challenges 276
4.1 Formalising Business Processes 277
4.2 Cost 278
4.3 System Acceptance 279
4.4 Information Overload 280
4.5 System Performance 280
5 Successful Implementations 281
5.1 AT& T’s Paperless Billing System
5.2 Royal Canadian Mounted Police’s Accounts Payable Departments 281
5.3 Siemens’ SAP Security Settings 282
5.4 Summary (Table 1) 283
6 Conclusion 283
References 285
Chapter 13: Towards a “Just-in-Time” Distributed Decision Support System in Health Care Research 288
1 Introduction 288
2 Literature Review 291
2.1 Health Research in Injury Prevention 291
2.2 Survey Approaches for Data Collection in Health Research 292
2.3 Decision Support Systems in Health Care 294
3 A Framework for a Decision-Making Process 294
4 Design and Development of the Just-in-Time AI-Based DSS 295
4.1 Just-in-Time Data Collection (JITDC) Module 296
4.1.1 Data Acquisition 296
4.1.2 Unified Survey Management System 297
4.1.2.1 Services Offered by the USMS 298
4.1.2.2 Architecture of the USMS 299
4.1.2.3 Data Analysis Unit 301
4.2 Artificial Intelligence (AI) Module 303
4.2.1 Data Definition 303
4.2.2 Knowledge Representation and Propagation 305
4.2.3 Agent Simulation 308
Simulation Set Up 308
Testing and Validation 310
Simulation Results 312
Effect of Social Network 312
Effect of Age Group 314
4.3 Persistent Storage 314
4.4 Business Logic and Decision Module 315
5 Conclusion 315
References 317
Chapter 14: Context Modelling in Time-Critical Decision Support for Medical Triage 321
1 Introduction 321
2 Context in Decision Making 322
3 Context Sensitive, Advisory Decision Support 324
4 Context in Time-Critical Decision Support 325
5 Context in Medical Triage 326
5.1 Modelling Context in Medical Triage 328
6 Implementation 330
7 System Evaluation and Discussion 331
8 Conclusions and Future Work 333
References 335
Chapter 15: Efficient Context Prediction for Decision Making in Pervasive Health Care Environments: A Case Study 337
1 Introduction 337
2 Motivating Application 338
3 The MUSIC Context Middleware 340
4 Learning Algorithms and Operators 343
5 Evaluation 345
5.1 Qualitative Evaluation 346
5.2 Quantitative Evaluation 347
6 Related Work 348
7 Conclusions 349
References 350
Chapter 16: On-demand Assistance in Handling Ammunition:Development of a Mobile Ammo DSS 352
1 Introduction 352
2 Background 353
2.1 Mobile Decision Support 353
2.2 Architectures of Mobile Decision Support 354
2.3 Mobile AME’s Context of Use 355
3 Mobile AME Design 356
3.1 Device Independence 357
3.2 Device usability 358
4 The Process of Decision Support for QASAS Personnel 358
5 Mobile AME System Architecture 359
5.1 Mobile AME Data Source 359
5.2 Server-Side Development 360
6 Mobile AME User Interface Design 360
6.1 Screen Design 361
6.2 Presentation of the Content 362
7 Application Navigation 362
8 Mobile AME Functionality 363
8.1 Ammunition, Inspection Points, Specification, Packaging and Shipping Information 364
8.2 Three-Dimensional Immersive Views 364
8.3 Inspection Point Video Clips 365
9 Exploratory Evaluation of Mobile AME 366
10 Discussion 366
11 Conclusion 367
References 368
Chapter 17: Development of a Mobile Situation Awareness Tool Supporting Disaster Recovery of Business Operations 370
1 Introduction 371
2 Related Work 373
3 Research Methodology 374
4 Situation Awareness (SA) Model 375
5 Situation Awareness (SA) Tool 376
6 Case Study 380
6.1 Definition 380
6.2 Preliminary Study 381
6.3 Requirements Elicitation 382
6.4 Theory/Tool Evaluation 384
6.5 Results from the Case Study 387
7 Discussion 388
8 Conclusions 389
References 390
Index 394