Intelligent database tools & applications : hyperinformation access, data quality, visualization, automatic discovery|RISS 상세보기

목차 (Table of Contents)

CONTENTS
1 INTELLIGENT DATABASES = 1
1.1 Introduction = 1
1.2 The Information Challenge = 8
1.3 People and Information = 11

CONTENTS
1 INTELLIGENT DATABASES = 1
1.1 Introduction = 1
1.2 The Information Challenge = 8
1.3 People and Information = 11
1.4 An Integrated Architecture for Intelligent Databases = 13
1.5 Areas of Application = 17
1.6 Roadmap of the Book = 19
1.7 What is New? = 20
1.8 Appendix : Trends in Database Technologies = 21
2 ARCHITECTURES AND METHODOLOGIES = 24
2.1 Introduction = 24
2.2 Usage of Intelligent Databases = 25
2.2.1 Querying and Reporting = 27
2.2.2 Presentation = 27
2.2.3 Visual Understanding = 29
2.2.4 Information Discovery = 30
2.2.5 Data Quality Managemet = 31
2.2.6 Text Management = 31
2.2.7 Data Fusion = 32
2.3 Architectures for Intelligent Database Applicatins = 33
2.3.1 Hardware Architecture = 34
2.3.2 Software Architecture = 36
2.3.3 Data Architecture = 40
2.4 Intellignet Database Development = 43
2.4.1 Traditional Database Design = 48
2.4.1.1 Data Modeling = 49
2.4.2 Concentric Design = 52
2.4.3 Building an Information Clearinghouse = 55
2.4.3.1 The Data Store = 57
2.4.3.2 Analysis and Access Tools = 60
2.4.3.3 The Data = 60
2.5 Methodologies for the Use of Intelligent Databases = 61
2.5.1 Models of Information Transmission = 61
2.5.2 The Hyperdata Model of Transmission Access = 62
2.5.3 Construction of Navigable Information = 64
2.5.4 Principles of Information Usage = 65
2.5.4.1 The Distill Step = 67
2.5.4.2 The Display Step = 69
2.5.4.3 The Interpret Step = 70
2.5.4.4 The select Step = 70
2.5.4.5 The Associate Step = 71
2.5.5 Intelligent Databases and Organizational Change = 72
2.6 Conclusion = 72
2.7 Appendix : Classes of Intelligent Database Applications = 73
2.7.1 Project Management = 73
2.7.2 Marketing= 74
2.7.3 Quality Control = 75
2.7.4 Financial Analysis = 77
2.7.5. Manufacturing = 78
2.7.6. Design = 79
2.7.7 Understanding, Prediction, and Control = 79
3 GRAPHICAL USER INTERFACES = 81
3.1 Introduction = 81
3.2 History and Evolution of User Interfaces = 84
3.2.1 History = 84
3.2.2 The Emergence of GUIs = 88
3.2.3 The GUI Model of Access = 94
3.2.3.1 Layers Within the User Interface = 94
3.2.3.2 User Interface Metaphors = 96
3.2.3.3 Windowing Environments = 98
3.2.3.4 Icons = 99
3.2.4 User Interface Development Tools = 101
3.3 Intelligent Database Interfaces = 104
3.3.1 Command Driven Interfaces and SQL = 108
3.3.2 Tables and Forms = 109
3.3.3 Graph-Based Query = 114
3.3.3.1 Visual SQL = 115
3.3.4 Querying with Icons = 118
3.3.4.1 Overview of Iconic Query = 119
3.3.4.2 scenarios = 120
3.3.4.3 Handling Joins = 121
3.3.4.4 Creation of the Iconic Schema = 121
3.3.4.5 Computation with iconic Query = 122
3.3.4.6 How Iconic Query Works = 123
3.3.5 Hypertext Querying = 125
3.4 The Future of User Interfaces = 128
3.5 Conclusion = 131
4 INFORMATION DISCOVERY = 132
4.1 Introduction = 132
4.2 History and Evolution of Data Analysis = 135
4.2.1 History = 136
4.2.2 The Evolution of Data Analysis = 139
4.2.3 The Modern Age of Discovery = 144
4.2.4 The Three stages of Discovery = 145
4.3 Automatic Rule Discovery = 147
4.3.1 Understanding the Character of a Database = 149
4.3.2 Rule Generation from Databases = 151
4.3.2.1 Rule Discovery = 153
4.3.2.2 Defining Goals and Interest levels = 154
4.3.2.3 Settig Discovery Parameters = 156
4.3.3 Rule Disovery Works = 157
4.3.3.1 Information Discovery on U.S. Import Data = 157
4.3.3.2 Information Discovery in the Oil Industry = 158
4.3.3.3 Information Discovery in Chemistry = 163
4.3.3.4 Information Discovery in Medical Research = 166
4.3.3.5 Information Discovery in Finance and Econometics = 168
4.3.3.6 Information Discovery in Sports Medicine = 171
4.4 Data Quality = 175
4.4.1 Quality Improvement = 176
4.4.2 Data Quality Enforcement = 177
4.4.2.1 Data Quality Audits = 178
4.4.2.2 Data Quality Programs = 179
4.4.3 Anomaly Detection in Databases = 180
4.4.3.1 Anomalies = 181
4.4.3.2 Detecting Errors and Anomalies = 181
4.4.3.3 Setting Tolerance Levels = 184
4.4.3.4 Anomaly Detection and Data Quality = 185
4.5 Prediction = 185
4.5.1 Prediction with Rules = 186
4.5.1.1 Interpreting the Results of Rule Discovery = 189
4.5.1.1.1 Too Few Sample Points = 189
4.5.1.1.2 Hidden Variable Effects = 189
4.5.2 Prediction with Statistics = 190
4.5.3 Neural Net Predictions = 191
4.6 A Comparative View = 193
4.6.1 Information Discovery and Statistics = 193
4.6.1.1 The Fisher Iris Sturdy = 195
4.6.2 Information Discovery and Neural Nets = 197
4.7 Conclusions = 200
5 DATA VISUALIZATION = 202
5.1 Introduction = 202
5.2 History and Evolution of Data Visualization = 204
5.2.1 Early Data Visualization = 204
5.2.2 Modern Data Visualization = 207
5.3 Visualization for Intelligent Databases = 208
5.3.1 Maps = 208
5.3.2 Coordinate-Based Charts = 209
5.3.2.1 Scattergrams = 211
5.3.2.2 Line Charts = 212
5.3.3 Ratio-Based charts = 213
5.3.3.1 Pie Charts = 213
5.3.3.2 Multivariate Profiles = 214
5.3.4 Hybrid Charts = 215
5.3.4.1 Bar Charts and Histograms = 215
5.3.4.2 Box Plots = 216
5.3.4.3 Hybrid Maps = 218
5.3.5 Iconic Charts = 218
5.3.6 Virtual Visualization = 219
5.3.7 Domain Specific Charts = 219
5.4 Methodologies for Data Visualization = 221
5.4.1 Data Transformation = 222
5.4.2 Data Analysis and Data Visualization = 224
5.4.3 Graphical Interpretation Tasks = 226
5.4.3.1 Magnitude and change = 227
5.4.3.2 Proportion = 227
5.4.3.3 Trend, Slope, and Correlation = 228
5.4.3.4 Groups and Outliers = 228
5.4.4 Data Visualization for Hyperdata = 229
5.5 Conclusions = 231
6 HYPERINFORMATION AND HYPERDATA = 233
6.1 Introduction = 233
6.2 The History and Evolution of Hyperinformation = 236
6.2.1 Definition = 236
6.2.2 The Hyperinformation Metaphor = 237
6.2.3 Early History = 237
6.2.4 Evolution of Hyperiformation = 239
6.3 Components of Hyperinformation = 240
6.3.1 Nodes = 240
6.3.1.1 Text Objects = 240
6.3.1.2 Data Objects = 242
6.3.2 Links = 243
6.3.2.1 Hierarchical Links = 244
6.3.2.2 Relational Liks = 244
6.3.3 Maps and Search Tools = 245
6.3.3.1 Book Functions = 245
6.3.3.2 Search Functions = 246
6.3.3.3 Maps and Organizers = 247
6.4 Hyperdata = 249
6.4.1 Hyperdata Nodes = 252
6.4.2 Hyperdata Links = 253
6.4.3 Hyperdata User Interface = 255
6.4.4 Inferential Hyperdata = 257
6.4.5 Hyperdata Navigation = 258
6.4.5.1 Rule Browsing = 258
6.4.5.2 Model Constraints = 261
6.4.6 Hyperdata Creation = 263
6.5 Hyperinformation Methodologies = 267
6.5.1 Navigation in Hyperinformation = 267
6.5.2 Disorientation = 270
6.5.3 A Model of Eploration = 271
6.6 Hyperinformation Research Issues = 273
6.6.1 A Critique of Hyperinformation = 273
6.6.1.1 A Solution in Sarch of a Task = 274
6.6.1.2 A Network in Search of a Browser = 274
6.6.1.3 Saling Up = 274
6.6.1.4 Going Nonlinear = 276
6.6.1.5 Bad Psychology = 277
6.6.1.6 Text is Tough = 278
6.6.1.7 Were's the Model and Method? = 278
6.6.2 Hypermedia and Hyperinformation Usability = 278
6.6.2.1 Research on Hypertext Usability = 279
6.6.2.2 Data Visualization in Hyperinformation = 281
6.6.2.3 Visual Menus and Hot Spots = 283
6.6.3 Current Trends = 284
6.6.3.1 The Merger of Electronic Document Technologies = 284
6.6.3.2 Hyperinformation as an Assistive Technology = 284
6.6.3.3 Embedded Systems = 285
6.7 Conclusion = 286
6.8 Appendix : Automated Hypertext Conversion = 287
6.8.1 Hypercompilation by Hyperterm Matching = 287
6.8.2 Project HEFTI = 289
6.8.3 Interactive Querying = 291
6.8.4 Statistical Representation of Meaning = 293
7 INFORMATION PRESENTATION = 295
7.1 Introduction = 295
7.2 History and Evolution of Information Presentation= 296
7.3 Presentation with Intelligent Databases = 298
7.3.1 Categories of Presentations = 299
7.3.2 Developing an Intelligent Presentation System = 301
7.3.3 Constructing Presentations = 305
7.3.4 Automated Presentation = 307
7.4 Methodologies for Presentation = 309
7.4.1 Information Processing = 310
7.4.2 Visualization and Presentation = 312
7.4.3 Graphical Templates = 313
7.4.4 Visualization for Presentation = 314
7.4.5 Presentation of Data in a Spatial Context = 315
7.4.6 Rules for Good Graphics = 316
7.4.7 Rotation and Color Coding = 318
7.5 Conclusions = 319
8 EXECUTIVE INFORMATION SYSTEMS = 320
8.1 Introduction = 320
8.2 History and Evolution of EISs = 322
8.2.1 Early EISs and Information Management Software = 325
8.2.2 Why Are Executive Information Systems Needed? = 326
8.2.3 General Functions of an EIS = 327
8.2.4 Prioritizing Information and Applying Strategic Focus = 328
8.3 Architecture of Executive Information Systems = 330
8.3.1 The Structure of Corporate Vision = 330
8.3.2 Information Discovery = 337
8.3.3 Hyperdata = 337
8.3.4 Information Overviews = 338
8.3.5 Interactive Dialog Construction = 339
8.3.6 Report Generation = 342
8.3.7 Using an Executive Information System = 343
8.4 Groupware and Computer Supported Collaborative Work = 344
8.4.1 Collaboration and Intelligent Databases = 346
8.4.2 Cooperative Document Editing = 347
8.4.3 Electronic Mail = 347
8.4.4 Voice Mail = 349
8.4.5 Videoconferencing = 350
8.5 Executive Information System Methodologies = 351
8.5.1 Drilling Down and Highlighting = 351
8.5.2 Flexible Query Processing = 352
8.5.3 Communication and Report Generation = 355
8.5.4 Detecting Patterns = 356
8.6 Executives as Information Users = 358
8.6.1 The Character of an Executive = 358
8.6.2 What Does and Excutive Do? = 359
8.6.3 Information Needs of Excutives = 362
8.6.4 Executive Tools = 363
8.7 Conclusions = 365
9 PROJECT MANAGEMENT AND VISUALIZATION = 367
9.1 Introduction = 367
9.2 History and Evolution of Project Management = 368
9.3 Basic Concepts of Progject Management = 370
9.3.1 Gantt Charts = 371
9.3.2 PERT/CPM = 372
9.3.3 Diagramming of Projects = 373
9.3.4 Dealing with Uncertain Durations = 373
9.3.5 Finding the Critical Path = 376
9.4 Intelligent Databases for Project Management = 379
9.4.1 Projects and Group Information = 379
9.4.2 Project Visualization = 381
9.4.2.1 3D Gantt Charts = 383
9.4.2.2 3D PERT Charts = 384
9.4.2.3 Multi-Subtask Status Charts = 385
9.4.2.4 Snake Charts = 385
9.4.2.5 Multi-Estimates Charts = 386
9.4.2.6 Traditional Charts = 386
9.4.3 Exploration and Project Visualization = 387
9.4.3.1 The Hyperdata Interface = 387
9.4.3.2 Hierarchical Charts and Drill Down = 388
9.4.3.3 Converting Project Data to Hyperdata = 389
9.4.3.4 Hyperdata and Dynamic Linking = 390
9.4.4 Using Hyperdata in Project Exploration = 391
9.4.5 The Merging of Projects and Presentations = 392
9.4.6 Presentations in Project Management = 393
9.4.7 Group-Oriented Project Management = 394
9.4.8 Requirements and Delivery = 397
9.4.9 Critical Risk Management = 400
9.5 Project Management Methodologies = 402
9.5.1 Project Monitoring and Control = 402
9.5.2 Product Management = 404
9.5.3 project planning = 405
9.6 Conclusions = 406
10 MARKETING = 407
10.1 Introduction = 407
10.2 History and Evolution of Maketing = 409
10.2.1 History = 409
10.3 Basic Concepts of Marketing = 415
10.3.1 The Marketing Problem= 416
10.3.2 Market Fragmentation = 419
10.3.3 Market Segmentation = 421
10.3.4 Competitive Analysis = 422
10.4 Sources of Market Data = 423
10.4.1 Product and Purchases Data = 424
10.4.1.1 Point-of-sale Data = 424
10.4.1.2 Looking Inside the Shopping Cart = 426
10.4.1.3 Accounting and Control Systems = 427
10.4.2 Consumer Data = 428
10.4.2.1 Census Data = 428
10.4.2.2 Psychographics = 428
10.4.2.3 Consumer Panels = 430
10.4.3 Geographic Data = 431
10.5 Intelligent Database Tools for Marketing = 432
10.5.1 Market Analysis with Corporate Vision and IDIS = 434
10.5.2 The Marketing Manager's View = 436
10.5.3 Segment Discovery = 438
10.5.4 POS Data Analysis = 442
10.6 Marketing Methodology = 445
10.6.1 Identifying the Customer = 445
10.6.2 Using Segments for Marketing Decisions = 447
10.6.3 Market Performance = 451
10.6.4 Targeted Marketing and Market Segmentation = 452
10.6.5 Product Positioning = 456
10.6.6 Identifying and Reaching Target Markets = 457
10.7 Conclusions = 458
11 INTELLIGENT QUALITY CONTROL = 459
11.1 Introduction = 459
11.2 History and Evolution of Quality = 460
11.3 Basic Concepts of Quality Control = 464
11.3.1 What is Quality? = 464
11.3.2 Forms of Quality = 466
11.3.3 Seven Tools of Quality Control = 467
11.3.3.1 Flow Charts = 469
11.3.3.2 Cause-and-Effect Diagrams = 470
11.3.3.3 Check sheets = 473
11.3.3.4 Histograms = 473
11.3.3.5 Scatter Diagrams = 475
11.3.3.6 Pareto Charts = 476
11.3.3.7 Control Charts = 479
11.3.4 Additional Quality Tools = 485
11.3.4.1 Affinity Diagrams= 486
11.3.4.2 Entity-Relationship Diagram = 486
11.3.4.3 Hierarchical Views = 486
11.3.4.4 Matrix Diagrams and Process Charts = 489
11.4 Intelligent Databases for Quality Control = 489
11.4.1 Quality and Executive Information Systems = 490
11.4.2 Quality Through Discovery = 491
11.4.2.1 Disk Drive Fault Analysis = 494
11.4.2.2 Materials Manufacturing = 495
11.4.2.3 Automobile Manufacturing= 497
11.4.3 Data Visualization = 500
11.4.3.1 The 2d Box Plot = 500
11.4.3.2 The 3D Bar Chart = 500
11.4.3.3 The 3D Box Diagram = 500
11.4.3.4 Hyperinformation = 501
11.4.4 Hyperinformation and Intelligent Quality Control = 503
11.5 Methodologies for Quality Control = 504
11.5.1 Management of Quality = 505
11.5.2 Detection is the Worst Form of Quality Control = 506
11.5.3 Total Quality Management = 507
11.6 Conclusions = 509
12 CONCLUSIONS = 511
12.1 Introduction = 511
REFERENCES = 513
INDEX = 525

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