Web-based Support Systems (eBook)

JingTao Yao (Herausgeber)

eBook Download: PDF

2010 | 2010
XXII, 440 Seiten
Springer London (Verlag)
978-1-84882-628-1 (ISBN)

Infos

Web-based Support Systems (WSS) are an emerging multidisciplinary research area in which one studies the support of human activities with the Web as the common platform,mediumandinterface.TheInternetaffectseveryaspectofourmodernlife. Moving support systems to online is an increasing trend in many research domains. One of the goals of WSS research is to extend the human physical limitation of information processing in the information age. Research on WSS is motivated by the challenges and opportunities arising from the Internet. The availability, accessibility and ?exibility of information as well as the tools to access this information lead to a vast amount of opportunities. H- ever, there are also many challenges we face. For instance, we have to deal with more complex tasks, as there are increasing demands for quality and productivity. WSS research is a natural evolution of the studies on various computerized support systems such as Decision Support Systems (DSS), Computer Aided Design (CAD), and Computer Aided Software Engineering (CASE). The recent advancement of computer and Web technologies make the implementation of more feasible WSS. Nowadays, it is rare to see a system without some type of Web interaction. The research of WSS is classi?ed into four groups. • WSS for speci?c domains.

Inhalt

Preface 5
Contents 9
List of Contributors 18
Part I Web-Based Support Systems for Specific Domains 22
1 Context-Aware Adaptation in Web-Based Groupware Systems 23
1.1 Introduction 24
1.1.1 The Web and the Collaboration Issue in Mobile Environment 24
1.1.2 Adaptation to Web-Based Groupware SystemsMobile Users 25
1.1.3 A Context- and Preference-Based Adaptationfor Web-Based Groupware Systems 26
1.1.4 Chapter Organization 27
1.2 Related Work 27
1.3 Context Representation 29
1.4 Representation of the Group Awareness Information 32
1.5 Representing User's Preferences 33
1.5.1 Filtering Rules 34
1.5.2 Context-Aware Profiles 36
1.5.3 Contextual Conditions 37
1.5.4 Personalizing Informational Content 38
1.5.5 Sharing Profiles 40
1.6 Filtering Process 41
1.6.1 Selecting Profiles 41
1.6.2 Selecting and Organizing Content 43
1.7 Implementation 46
1.7.1 BW-M Framework 46
1.7.2 Implementation Issues 47
1.8 Conclusion 48
References 49
2 Framework for Supporting Web-Based Collaborative Applications 52
2.1 Introduction 52
2.1.1 Barriers and Obstacles 53
2.1.2 Research Motivations 53
2.1.3 Benefits 54
2.1.4 Research Questions and Aims 54
2.2 Research Background 54
2.2.1 Service System 54
2.2.2 Dynamic Re-configurable System 55
2.3 Solution Approach 56
2.3.1 Dynamic Services Management 56
2.3.2 Service Availability 57
2.3.3 Services Invocation and Execution 57
2.4 Application – Web-based Solution for e-Health 58
2.5 Conclusion 60
References 60
3 Helplets: A Common Sense-Based Collaborative Help Collection and Retrieval Architecture for Web-Enabled Systems 62
3.1 Introduction 63
3.2 Issues in Contemporary Help Systems 64
3.2.1 Tutorial Embedding 65
3.2.2 Tutorial Decomposition 65
3.3 Machine Common Sense 65
3.4 Helplet Architecture 67
3.4.1 Knowlege Collection: Helplets 69
3.4.2 Knowledge Organization: Folksonomy 69
3.4.3 Knowledge Retrieval: Common Sense 71
3.4.3.1 Machine Common Sense and Helplets 72
3.4.3.2 Central Problems of Folksonomy 72
3.4.3.3 Flow of Control 72
3.4.3.4 User Preferences 73
3.4.3.5 Score Vector 74
3.4.3.6 Issues in the Basic Approach 75
3.4.3.7 Modifications to Personalized Web Search 75
3.4.3.8 Enhancing the Basic Technique 76
3.5 Related Work 79
3.6 Conclusion 81
References 82
4 Web-based Virtual Research Environments 84
4.1 Introduction 84
4.2 Short Review of VREs 86
4.3 Our Experience of VRE 90
4.3.1 Architecture 90
4.3.2 The Sakai Collaborative Learning Framework 91
4.3.3 Prototype: Sakai VRE Portal Demonstrator 92
4.3.4 Production: Psi-k VRE 95
4.3.5 Production: Social Science e-Infrastructure VRE 96
4.4 Further Discussion and Summary 97
References 98
5 Web-Based Learning Support System 100
5.1 Introduction 100
5.2 Learning and Learning Support Systems 101
5.3 Functions of Web-based Learning Support Systems 102
5.4 Designs and Implementation of a WLSS 104
5.5 The Proposed Framework Based on KSM 107
5.6 Rough set-based Learning Support to Predict Academic Performance 108
5.6.1 Survey and Data Collection 109
5.6.2 Results and Discussion 110
5.7 Conclusion 112
References 113
6 A Cybernetic Design Methodology for `Intelligent' Online Learning Support 115
6.1 Introduction 115
6.2 Rationale 117
6.3 The Need for `Intelligent' Cognition Support Systems 118
6.4 Metacognition as the Primary Learning Goal 121
6.5 A Brief History of Cognition Support Systems 124
6.6 Enabling Effective Cognition and Metacognition Development 128
6.7 Relationships and Connectedness: Pathways to Meaning 130
6.8 A Model for Constructing ``Intelligent'' CognitionSupport Systems 133
6.9 Conclusion 137
6.10 Research Questions for Further Study 139
References 140
7 A Web-Based Learning Support System for Inquiry-BasedLearning 143
7.1 Introduction 143
7.2 Web-Based Learning Support Systems and Inquiry-BasedLearning 144
7.2.1 Web-Based Learning Support Systems 144
7.2.2 Web Services 145
7.2.3 Online-Learning Games 145
7.2.4 Inquiry-Based Learning 146
7.2.5 Web-Based Learning Support Systems for Inquiry-Based Learning 147
7.3 Modeling Online Treasure Hunt 147
7.3.1 Treasure Hunts 147
7.3.2 Treasure Hunt Model for Inquiry-Based Learning 148
7.4 Implementation of Online Treasure Hunt 150
7.4.1 Architecture of Online Treasure Hunt 150
7.4.2 Teaching Support Subsystem 151
7.4.3 Learning Support Subsystem 152
7.4.4 Treasure Hunt Game 153
7.4.5 Treasure Hunt Process 154
7.5 A Demonstrative Example of the System 155
7.6 Conclusion 159
References 160
Part II Web-Based Applications and WSS Techniques 162
8 Combinatorial Fusion Analysis for Meta Search InformationRetrieval 163
8.1 Introduction 163
8.2 Combinatorial Fusion Analysis 167
8.2.1 Multiple Scoring Systems 167
8.2.2 Rank/Score Function and the Rank-Score Characteristics (RSC) Graph 168
8.2.3 Rank and Score Combination 171
8.2.4 Performance Evaluation 172
8.2.5 Diversity 175
8.3 Combinatorial Fusion Analysis Applications in Information Retrieval 176
8.3.1 Predicting Fusion Results 176
8.3.2 Comparing Rank and Score Combination 177
8.4 Conclusion and Future Work 178
References 179
9 Automating Information Discovery Within the Invisible Web 182
9.1 Introduction 183
9.2 The Deep Web 184
9.3 State of the Art in Searching the Deep Web 187
9.3.1 Automatic Information Discovery from theInvisible Web 188
9.3.2 Query Routing: Finding Ways in the Mazeof the Deep Web 189
9.3.3 Downloading the Hidden Web Content 190
9.3.4 Information Discover, Extraction, and Integration for Hidden Web 193
9.4 Conclusion 195
References 195
10 Supporting Web Search with Visualization 197
10.1 Web Search and Web Support Systems 197
10.2 Web Information Retrieval 198
10.2.1 Traditional Information Retrieval 198
10.2.2 Information Retrieval on the Web 199
10.2.3 Web Search User Interfaces 200
10.2.4 Web Search User Behaviour 201
10.3 Issues in Information Visualization 202
10.4 A Taxonomy of Information to Support Web Search Processes 204
10.4.1 Attributes of the Query 204
10.4.2 Attributes of the Document Surrogate 205
10.4.3 Attributes of the Document 205
10.4.4 Attributes of the Search Results Set 205
10.4.5 External Knowledge Bases 206
10.5 Challenges in Search Representations 206
10.6 Seminal and State-of-the-Art Research in Visual Web Search 208
10.6.1 Query Visualization 208
10.6.2 Search Results Visualization 212
10.6.2.1 Document Visualization 213
10.6.2.2 Document Surrogate Visualization 216
10.6.3 Revisiting the Taxonomy of Information 223
10.7 Conclusions 224
References 225
11 XML Based Markup Languages for Specific Domains 229
11.1 Background 230
11.1.1 XML: The eXtensible Markup Language 230
11.1.1.1 Need for XML 230
11.1.1.2 XML Terminology 231
11.1.2 Domain-Specific Markup Languages 232
11.1.2.1 Examples of Domain-Specific Markup Languages 233
11.1.2.2 MatML: The Materials Markup Language 234
11.2 Development of Markup Languages 236
11.2.1 Acquisition of Domain Knowledge 236
11.2.2 Data Modeling 237
11.2.2.1 Entity Relationship Diagram 237
11.2.3 Requirements Specification 237
11.2.4 Ontology Creation 238
11.2.5 Revision of the Ontology 240
11.2.6 Schema Definition 240
11.2.7 Reiteration of the Schema 241
11.3 Desired Properties of Markup Languages 243
11.3.1 Avoidance of Redundancy 243
11.3.2 Non-ambiguous Presentation of Information 243
11.3.3 Easy Interpretability of Information 244
11.3.4 Incorporation of Domain-Specific Requirements 244
11.3.5 Potential for Extensibility 245
11.4 Application of XML Features in Language Development 245
11.4.1 Sequence Constraint 245
11.4.2 Choice Constraint 246
11.4.3 Key Constraint 246
11.4.4 Occurrence Constraint 247
11.5 Convenient Access to Information 249
11.5.1 XQuery: XML Query Language 249
11.5.2 XSLT: XML Style Sheet Language Transformations 250
11.5.3 XPath: XML Path Language 250
11.6 Conclusions 250
References 251
12 Evaluation, Analysis and Adaptation of Web Prefetching Techniques in Current Web 253
12.1 Introduction to Web Prefetching 253
12.1.1 Generic Web Architecture 254
12.1.2 Prediction Engine 255
12.1.3 Prefetching Engine 256
12.1.4 Web Prediction Algorithms 256
12.1.4.1 Prediction from the Access Pattern 256
12.1.4.2 Prediction from Web Content 257
12.2 Performance Evaluation 257
12.2.1 Experimental Framework 257
12.2.1.1 Surrogate 258
12.2.1.2 Client 260
12.2.1.3 Proxy Server 262
12.2.2 Performance Key Metrics 263
12.2.2.1 Prediction Related Indexes 264
12.2.2.2 Resource Usage Indexes 266
12.2.2.3 Latency Related Indexes 268
12.2.3 Comparison Methodology 268
12.2.4 Workload 270
12.3 Evaluation of Prefetching Algorithms 271
12.3.1 Prefetching Algorithms Description 271
12.3.2 Experimental Results 274
12.3.2.1 Latency Per Page Ratio 274
12.3.2.2 Space 275
12.3.2.3 Processor Time 275
12.3.3 Summary 276
12.4 Theoretical Limits on Performance 276
12.4.1 Metrics 276
12.4.2 Predicting at the Server 278
12.4.3 Predicting at the Client 279
12.4.4 Predicting at the Proxy 280
12.4.5 Prefetching Limits Summary 281
12.5 Summary and Conclusions 282
References 282
13 Knowledge Management System Based on Web 2.0 Technologies 286
13.1 Introduction 286
13.2 Knowledge Management Systems 287
13.3 Web 2.0 290
13.4 Rich Internet Applications Architecture 292
13.5 Rich Internet Application Frameworks 293
13.6 Developing a Knowledge-Based Management System 301
13.7 Implementing a Knowledge Management System 306
13.8 Case Study: The RV10 Project 307
13.9 Conclusions 312
References 313
Part III Design and Development of Web-Based Support Systems 315
14 A Web-Based System for Managing Software ArchitecturalKnowledge 316
14.1 Introduction 316
14.2 Background and Motivation 317
14.2.1 Architecture-Based Software Development 318
14.2.2 Knowledge Management Issues in Software Architecture Process 319
14.2.3 Support for Architectural Knowledge Management 320
14.3 Tool Support for Managing Architectural Knowledge 321
14.3.1 The Architecture of PAKME 321
14.3.2 The Data Model of PAKME 323
14.3.3 Implementation 324
14.4 Managing Architectural Knowledge with PAKME 326
14.4.1 Capturing and Presenting Knowledge 327
14.4.2 Supporting Knowledge Use/Reuse 330
14.5 An Industrial Case of Using PAKME 333
14.5.1 Use of PAKME's Knowledge Base 335
14.5.2 Use of PAKME's Project Base 335
14.5.3 Observations from the Study 336
14.6 Related Work 339
14.7 Summary 340
References 341
15 CoP Sensing Framework on Web-Based Environment 344
15.1 Introduction 344
15.2 Community of Practice (CoP) Characteristics 346
15.3 CoP Objects in the Social Learning Framework 349
15.4 Web-Based System for Sensing Social Learning Framework 349
15.4.1 Community Structure 351
15.4.1.1 Volatility of the Membership 351
15.4.1.2 Temporal Domination in the Community Participation Hierarchy 352
15.4.1.3 Existence of Common Interest 353
15.4.1.4 Common Interest – Activity 353
15.4.1.5 Common Interest – Communication 354
15.4.1.6 Common Interest – Relationship 355
15.4.1.7 Fluid Movement Between Groups 355
15.4.2 Learning Through Participation and Reification 356
15.4.3 Negotiation of Meaning 358
15.4.4 Learning as Temporal 359
15.4.5 Boundary Objects and Boundary Encounters 360
15.4.6 Mutual Engagement, Joint Enterprise, and Shared Repertoire 361
15.4.7 Identity 364
15.5 Integrated Schema of the Entire System 365
15.6 Conclusion 366
References 366
16 Designing a Successful Bidding Strategy Using Fuzzy Sets and Agent Attitudes 369
16.1 Introduction 369
16.2 Related Works 370
16.3 A Fuzzy Bidding Strategy (FA-Bid) 372
16.3.1 Basic Scenario 372
16.3.2 FA-Bid Overview 373
16.3.3 Attribute Evaluation 374
16.3.3.1 Weights Determination 374
16.3.3.2 Assessment Expression 374
16.3.3.3 Assessments Aggregation 374
16.3.4 Attitude Estimation 376
16.3.5 Overall Assessment 376
16.3.6 Agent Price Determination 377
16.4 Conclusions 378
References 379
17 Design Scenarios for Web-Based Management of OnlineInformation 381
17.1 Introduction 382
17.2 Scenario-Based Development 383
17.3 Understanding Design Opportunities 385
17.4 Current Technologies 389
17.4.1 Input 390
17.4.2 Output 391
17.4.3 Portability 391
17.5 Towards New Designs 392
17.6 Discussion 392
References 395
18 Data Mining for Web-Based Support Systems: A Case Study in e-Custom Systems 397
18.1 Introduction 397
18.2 Data Mining as a Part of the Decision Making Process 399
18.3 Building Blocks for New Web-Based Support Systems: Web Services, SOA, Smart Seals 402
18.3.1 Web Services 402
18.3.2 Service-Oriented Architecture (SOA) 403
18.3.3 Smart Seals: TREC or RFID Technology 404
18.4 Web-Based Support Systems for e-Business and e-Custom 405
18.5 Evaluation and Discussion 407
18.6 Conclusions 410
References 411
19 Service-Oriented Architecture (SOA) as a Technical Framework for Web-Based Support Systems (WSS) 413
19.1 Introduction 413
19.2 Support Systems: A Historical Perspective 414
19.3 Service as a Medium of Information Exchange for Web-Based Support Systems 415
19.3.1 Genesis of a `Service' as Data/Input AccessCharacteristic 416
19.3.2 `Service': A Short Primer 417
19.3.3 Service-Oriented Inputs as Digestible Units for a Support System 417
19.3.4 Service-Oriented Information as Fine-GrainedOutput Decision Stream Services 418
19.4 Service-Oriented Architecture (SOA): An Architectural Evolution for Data Access 419
19.4.1 Service-Oriented Architecture (SOA) 419
19.4.2 Rise of a Web Service: Software as a Service(SaaS) Over the Internet 421
19.5 SOA: The Information Gateway for Support Systems 422
19.5.1 Enterprise Service Bus: SOA's Elixir for DataAccess for Support Systems 422
19.5.1.1 Inside the Enterprise Service Bus 423
19.5.2 AirMod-X: A Support System Example 424
19.5.2.1 Challenge Scenario – 1: The Traditional Way 424
19.5.2.2 Challenge Scenario – 2: The SOA Way 425
19.5.3 SOA and WSS: An Interplay 426
19.6 Technologies for SOA Implementation for WSS 430
19.6.1 Sample WSS Scenario: AirMod-X 431
19.7 Conclusion 435
References 436
A Contributor's Biography 438
Index 447

Produkt-Details

• Erscheint lt. Verlag: 2.3.2010
• Reihe/Serie: Advanced Information and Knowledge Processing
• Reihe/Serie: Advanced Information and Knowledge Processing
• Zusatzinfo: XXII, 440 p. 160 illus.
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Mathematik / Informatik ► Informatik ► Grafik / Design
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Schlagworte: Data Mining • Framework • Information Retrieval • Information Technology • knowledge management • Support systems • techniques • Web • Web technology • WSS • XML
• ISBN-10: 1-84882-628-1 / 1848826281
• ISBN-13: 978-1-84882-628-1 / 9781848826281