Item Details

Document Warehousing and Text Mining

Dan Sullivan
Format
Book
Published
New York : Wiley, c2001.
Language
English
ISBN
0471399590 (pbk. : alk. paper)
Contents
  • Part 1 Text Analysis for Business Intelligence 1
  • Chapter 1 Expanding the Scope of Business Intelligence 3
  • Need to Deal with Text 3
  • Growth of Textual Information--The Good News 6
  • Growth of Textual Information--The Bad News 7
  • Finding Information: It's Not as Easy as It Used to Be 7
  • Beware What You Wish for: Finding Too Much Information 9
  • Document Warehousing Approach to the Information Glut 9
  • Supporting Business Intelligence with Text 10
  • Defining the Document Warehouse 11
  • Role of Text Mining in Document Warehousing 20
  • Building the Document Warehouse 22
  • Benefits of Document Warehousing 24
  • Chapter 2 Understanding the Structure of Text: The Foundation of Text-Based Business Intelligence 29
  • Myth of Unstructured Texts 30
  • Natural Structures: It's All in Your Head 31
  • Building Blocks of Language 31
  • Working with Statistical Techniques 42
  • Macrostructures: Introducing Artificial Structures in Documents 43
  • Hierarchical Conventions from Words to Documents 45
  • Jewel in the Crown: Markup Languages for Arbitrary Structure 46
  • It Isn't So Linear After All: Hypertext 50
  • Chapter 3 Exploiting the Structure of Text 55
  • Text-Oriented Business Intelligence Operations 56
  • Summarizing Documents 57
  • Classifying and Routing Documents 58
  • Answering Questions 60
  • Searching and Browsing by Topic and Theme 61
  • Searching by Example 64
  • Text-Oriented Business Intelligence Techniques 65
  • Full Text Searching: Text Processing 101 65
  • Undirected Summarization 70
  • Document Clustering 71
  • Integration with the Data Warehouse 74
  • Dimensional Models: A Quick Refresher 74
  • Integration with the World Wide Web 76
  • Adapting to Changing Users' Interests 77
  • Part 2 Document Warehousing 79
  • Chapter 4 Overview of Document Warehousing 81
  • Meeting Business Intelligence Requirements 82
  • Who Are the End Users? 82
  • What Information Is Needed? 82
  • When Is It Needed? 83
  • Where Is the Information Found? 84
  • Role of the Document Warehouse in Business Intelligence 84
  • Architecture of the Document Warehouse 85
  • Document Sources 86
  • Text Processing Servers 89
  • Text Databases and Other Storage Options 92
  • Metadata Repositories 93
  • User Profiling 94
  • Process of Document Warehousing 96
  • Identifying Document Sources 96
  • Document Retrieval 98
  • Preprocessing Operations 99
  • Text Analysis Operations 101
  • Managing the Document Warehouse 101
  • Supporting End-User Operations 102
  • Chapter 5 Meeting Business Intelligence Requirements: More Than Just Numbers 103
  • A Variety of Problems to Choose From 104
  • Intelligent Document Management 104
  • Historical Reporting and Trend Analysis 105
  • Market Monitoring 106
  • Competitive Intelligence 107
  • Defining the Business Objectives 108
  • Getting What You Want from Your Text 108
  • Answering the Right Business Questions 112
  • Determining Who Will Use the System 114
  • Extracting the Right Information for Future Processing and Searching 115
  • Classifying Documents for Browsing 115
  • Setting the Scope 117
  • Time Requirements 118
  • Space Requirements and Sizing the Document Warehouse 119
  • Creating the Document Warehouse Project Plan 120
  • Design and Development 120
  • Chapter 6 Designing the Document Warehouse Architecture 123
  • Document Sources 124
  • File Servers 125
  • Document Management Systems 129
  • Internet Resources 134
  • From Document Sources to Text Analysis 135
  • Text Processing Servers 136
  • Using Crawlers and Agents to Retrieve Documents 136
  • Text Analysis Services 140
  • Document Warehouse Storage Options 141
  • Database Options 142
  • Metadata Repository and Document Data Model 144
  • Document Content Metadata 144
  • Search and Retrieval Metadata 145
  • Text Mining Metadata 147
  • Storage Metadata 148 --s Document Data Model 149
  • User Profiles and End-User Support 150
  • End-User Profiles 153
  • Data Warehouse and Data Mart Integration 155
  • Linking Numbers and Text 156
  • Integration Heuristics 157
  • Chapter 7 Finding and Retrieving Relevant Text 159
  • Manual Retrieval Methods 160
  • Search Tools 161
  • Automatic Retrieval Methods 163
  • Data-Driven Searching 163
  • Searching Internal Networks 164
  • Configuring Crawlers 165
  • Batch versus Interactive Retrieval 169
  • Retrieving from Document Processing Systems 171
  • Tradeoffs between Manual and Automatic Retrieval 171
  • Precision 172
  • Recall 172
  • Cost 173
  • Effectiveness 173
  • Text Management Issues 174
  • Avoiding Duplication 174
  • Accommodating Document Revisions and Versioning 175
  • Assessing the Reliability of a Source 175
  • Improving Performance 176
  • Representing Users' Areas of Interest 176
  • Data Store for Interest Specifications 177
  • Creating Interest Specifications 178
  • Interest Specifications Drive Searching 180
  • Prototype-Driven Searching 181
  • Chapter 8 Loading and Transforming Documents 183
  • Internationalization and Character Set Issues 184
  • Coded Character Sets 185
  • Translating Documents 186
  • Indexing Text 195
  • Full Text Indexing 195
  • Thematic Indexing 196
  • Document Classification 198
  • Labeling 198
  • Multidimensional Taxonomies 200
  • Document Clustering 201
  • Binary Relational Clustering 202
  • Hierarchical Clustering 202
  • Self-Organizing Map Clustering 202
  • Summarizing Text 204
  • Basic Summarization Methods 205
  • Dealing with Large Documents 206
  • Chapter 9 Managing Document Warehouse Metadata 209
  • Metadata Standards 210
  • Common Warehouse Model 212
  • Knowledge Management Based on the Open Information Model 221
  • Dublin Core 223
  • Adapting Metadata Standards to Document Warehousing 228
  • Content Metadata 228
  • Technical Metadata 230
  • Controlling Document Loads in the Warehouse 231
  • Prioritizing Items in Multiple Processing Queues 233
  • Summarizing Documents 234
  • Business Metadata 235
  • Quality: Timeliness and Reliability 236
  • Access Control 236
  • Versioning 237
  • Chapter 10 Ensuring Document Warehouse Integrity 239
  • Information Stewardship and Quality Control 240
  • Document Search and Retrieval 241
  • Text Analysis 248
  • Content Validation 253
  • Security 254
  • File System Security 255
  • Database Roles and Privileges 255
  • Programmatic Access Control 255
  • Virtual Database Security 256
  • Privacy 258
  • Contracts between Document Owners and the Warehouse 258
  • Is Privacy the Third Rail of Business Intelligence? Protecting Individuals and Organizations 259
  • Chapter 11 Choosing Tools for Building the Document Warehouse 261
  • Choosing Text Analysis Tools 262
  • Statistical/Heuristic Approach 264
  • Knowledge-Based Approach 275
  • Neural Network Approach: Megaputer's TextAnalyst 287
  • There Is More Than One Way to Mine Text 294
  • Choosing Supplemental Tools 298
  • Choosing Web Document Retrieval Tools 301
  • Chapter 12 Developing a Document Warehouse: A Checklist 305
  • Step 1 What Should Be Stored? 306
  • Understanding User Needs 306
  • Defining Document Sources 307
  • Metadata 308
  • User Profiles 309
  • Integration with the Data Warehouse 309
  • Step 2 Where Should It Be Stored? 310
  • Step 3 What Text Mining Services Should Be Used? 312
  • Indexing Services 313
  • Feature Extraction 314
  • Summarization 314
  • Document Clustering 314
  • Question Answering 315
  • Classification and Routing 315
  • Building Taxonomies and Thesauri 316
  • Step 4 How Should the Warehouse Be Populated? 316
  • Crawlers 317
  • Searching 318
  • Step 5 How Should the Warehouse Be Maintained? 319
  • Part 3 Text Mining 321
  • Chapter 13 What Is Text Mining? 323
  • Defining Text- Mining 324
  • Foundations of Text Mining 326
  • Information Retrieval 327
  • Computational Linguistics and Natural Language Processing 341
  • Discovering Knowledge in Text: Example Cases 351
  • Text Mining Methodology: Using the Cross-Industry Process Model for Data Mining 356
  • Business Understanding 358
  • Data Understanding 359
  • Data Preparation 360
  • Modeling 361
  • Evaluation 362
  • Deployment 362
  • Adopting the CRISP-DM to Text Mining 363
  • Text Mining Applications 365
  • Knowing Your Business 366
  • Knowing Your Customer 366
  • Knowing Your Competition and Market 367
  • Chapter 14 Know Thyself: Using Text Mining for Operational Management 369
  • Operations and Projects: Understanding the Distinct Needs of Each 371
  • Enterprise Document Management Systems 374
  • Benefits of Enterprise Document Management Systems 374
  • Limits of Enterprise Document Management Systems 375
  • Integrating Document Management with Document Warehousing 378
  • Document Extraction 378
  • Steps to Effective Text Mining for Operational Management 381
  • Specifying a Process for Extracting Information 381
  • Meeting Wide-Ranging Organizational Needs 384
  • Chapter 15 Knowing Your Business-to-Business Customer: Text Mining for Customer Relationship Management 387
  • Understanding Your Customer's Market 388
  • Developing a Customer Intelligence Profile 389
  • Sample Case of B-to-B Customer Relationship Management 391
  • Getting the Information I: Internal Sources 391
  • Getting the Information II: External Documents 397
  • Collecting External Documents 398
  • Preliminary Document Analysis 399
  • Chapter 16 Text Mining for Competitive Intelligence 407
  • Competitive Intelligence versus Business Intelligence 408
  • Competitive Intelligence Profiles 410
  • Identifying Information Sources 413
  • XML Text Processing Operations 416
  • XML Interface Models 417
  • -- Getting Financial Information from XBRL Documents 423
  • Practice of Competitive Intelligence 425
  • Competitive Intelligence in Health Care: Patent Analysis 426
  • Competitive Intelligence in Manufacturing: Financial Analysis 431
  • Competitive Intelligence in Financial Services Market: Market Issue Analysis 433
  • Chapter 17 Text Mining Tools 437
  • Criteria for Choosing Tools 438
  • Preprocessing Tools 438
  • Text Mining Tool Selection 446
  • Still Looking for a Silver Bullet: The Limits of Text Mining 453
  • Discourse Analysis 454
  • Semantic Models 455
  • Chapter 18 Changes in Business Intelligence 459
  • Business Intelligence and the Dynamics of Organizations 459
  • Changing Decision Makers 461
  • Changing Technologies 462
  • Changing Strategies 463
  • Meeting BI Needs with the Document Warehouse and Text Mining 466
  • Process of Document Warehousing 466
  • Text Mining for Decision Support 474
  • Text Mining and Operational Management 475
  • Text Mining and Customer Relationship Management 475
  • Text Mining and Competitive Intelligence 476
  • Shifting Emphasis of BI 478
  • Text, Not Just Numbers 478
  • Heuristics, Not Just Algorithms 478
  • Distributed Intelligence, Centralized Management 479
  • Next Steps: Where Do We Go from Here? 479
  • Appendix C Basic Document Warehouse Data Model 497.
Description
xviii, 542 p. : ill. ; 24 cm.
Notes
Includes bibliographical references (p. 515-522) and index.
Technical Details
  • Access in Virgo Classic

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    g| Part 1 t| Text Analysis for Business Intelligence g| 1 -- g| Chapter 1 t| Expanding the Scope of Business Intelligence g| 3 -- t| Need to Deal with Text g| 3 -- t| Growth of Textual Information--The Good News g| 6 -- t| Growth of Textual Information--The Bad News g| 7 -- t| Finding Information: It's Not as Easy as It Used to Be g| 7 -- t| Beware What You Wish for: Finding Too Much Information g| 9 -- t| Document Warehousing Approach to the Information Glut g| 9 -- t| Supporting Business Intelligence with Text g| 10 -- t| Defining the Document Warehouse g| 11 -- t| Role of Text Mining in Document Warehousing g| 20 -- t| Building the Document Warehouse g| 22 -- t| Benefits of Document Warehousing g| 24 -- g| Chapter 2 t| Understanding the Structure of Text: The Foundation of Text-Based Business Intelligence g| 29 -- t| Myth of Unstructured Texts g| 30 -- t| Natural Structures: It's All in Your Head g| 31 -- t| Building Blocks of Language g| 31 -- t| Working with Statistical Techniques g| 42 -- t| Macrostructures: Introducing Artificial Structures in Documents g| 43 -- t| Hierarchical Conventions from Words to Documents g| 45 -- t| Jewel in the Crown: Markup Languages for Arbitrary Structure g| 46 -- t| It Isn't So Linear After All: Hypertext g| 50 -- g| Chapter 3 t| Exploiting the Structure of Text g| 55 -- t| Text-Oriented Business Intelligence Operations g| 56 -- t| Summarizing Documents g| 57 -- t| Classifying and Routing Documents g| 58 -- t| Answering Questions g| 60 -- t| Searching and Browsing by Topic and Theme g| 61 -- t| Searching by Example g| 64 -- t| Text-Oriented Business Intelligence Techniques g| 65 -- t| Full Text Searching: Text Processing 101 g| 65 -- t| Undirected Summarization g| 70 -- t| Document Clustering g| 71 -- t| Integration with the Data Warehouse g| 74 -- t| Dimensional Models: A Quick Refresher g| 74 -- t| Integration with the World Wide Web g| 76 -- t| Adapting to Changing Users' Interests g| 77 -- g| Part 2 t| Document Warehousing g| 79 -- g| Chapter 4 t| Overview of Document Warehousing g| 81 -- t| Meeting Business Intelligence Requirements g| 82 -- t| Who Are the End Users? g| 82 -- t| What Information Is Needed? g| 82 -- t| When Is It Needed? g| 83 -- t| Where Is the Information Found? g| 84 -- t| Role of the Document Warehouse in Business Intelligence g| 84 -- t| Architecture of the Document Warehouse g| 85 -- t| Document Sources g| 86 -- t| Text Processing Servers g| 89 -- t| Text Databases and Other Storage Options g| 92 -- t| Metadata Repositories g| 93 -- t| User Profiling g| 94 -- t| Process of Document Warehousing g| 96 -- t| Identifying Document Sources g| 96 -- t| Document Retrieval g| 98 -- t| Preprocessing Operations g| 99 -- t| Text Analysis Operations g| 101 -- t| Managing the Document Warehouse g| 101 -- t| Supporting End-User Operations g| 102 -- g| Chapter 5 t| Meeting Business Intelligence Requirements: More Than Just Numbers g| 103 -- t| A Variety of Problems to Choose From g| 104 -- t| Intelligent Document Management g| 104 -- t| Historical Reporting and Trend Analysis g| 105 -- t| Market Monitoring g| 106 -- t| Competitive Intelligence g| 107 -- t| Defining the Business Objectives g| 108 -- t| Getting What You Want from Your Text g| 108 -- t| Answering the Right Business Questions g| 112 -- t| Determining Who Will Use the System g| 114 -- t| Extracting the Right Information for Future Processing and Searching g| 115 -- t| Classifying Documents for Browsing g| 115 -- t| Setting the Scope g| 117 -- t| Time Requirements g| 118 -- t| Space Requirements and Sizing the Document Warehouse g| 119 -- t| Creating the Document Warehouse Project Plan g| 120 -- t| Design and Development g| 120 -- g| Chapter 6 t| Designing the Document Warehouse Architecture g| 123 -- t| Document Sources g| 124 -- t| File Servers g| 125 -- t| Document Management Systems g| 129 -- t| Internet Resources g| 134 -- t| From Document Sources to Text Analysis g| 135 -- t| Text Processing Servers g| 136 -- t| Using Crawlers and Agents to Retrieve Documents g| 136 -- t| Text Analysis Services g| 140 -- t| Document Warehouse Storage Options g| 141 -- t| Database Options g| 142 -- t| Metadata Repository and Document Data Model g| 144 -- t| Document Content Metadata g| 144 -- t| Search and Retrieval Metadata g| 145 -- t| Text Mining Metadata g| 147 -- t| Storage Metadata g| 148 --s t| Document Data Model g| 149 -- t| User Profiles and End-User Support g| 150 -- t| End-User Profiles g| 153 -- t| Data Warehouse and Data Mart Integration g| 155 -- t| Linking Numbers and Text g| 156 -- t| Integration Heuristics g| 157 -- g| Chapter 7 t| Finding and Retrieving Relevant Text g| 159 -- t| Manual Retrieval Methods g| 160 -- t| Search Tools g| 161 -- t| Automatic Retrieval Methods g| 163 -- t| Data-Driven Searching g| 163 -- t| Searching Internal Networks g| 164 -- t| Configuring Crawlers g| 165 -- t| Batch versus Interactive Retrieval g| 169 -- t| Retrieving from Document Processing Systems g| 171 -- t| Tradeoffs between Manual and Automatic Retrieval g| 171 -- t| Precision g| 172 -- t| Recall g| 172 -- t| Cost g| 173 -- t| Effectiveness g| 173 -- t| Text Management Issues g| 174 -- t| Avoiding Duplication g| 174 -- t| Accommodating Document Revisions and Versioning g| 175 -- t| Assessing the Reliability of a Source g| 175 -- t| Improving Performance g| 176 -- t| Representing Users' Areas of Interest g| 176 -- t| Data Store for Interest Specifications g| 177 -- t| Creating Interest Specifications g| 178 -- t| Interest Specifications Drive Searching g| 180 -- t| Prototype-Driven Searching g| 181 -- g| Chapter 8 t| Loading and Transforming Documents g| 183 -- t| Internationalization and Character Set Issues g| 184 -- t| Coded Character Sets g| 185 -- t| Translating Documents g| 186 -- t| Indexing Text g| 195 -- t| Full Text Indexing g| 195 -- t| Thematic Indexing g| 196 -- t| Document Classification g| 198 -- t| Labeling g| 198 -- t| Multidimensional Taxonomies g| 200 -- t| Document Clustering g| 201 -- t| Binary Relational Clustering g| 202 -- t| Hierarchical Clustering g| 202 -- t| Self-Organizing Map Clustering g| 202 -- t| Summarizing Text g| 204 -- t| Basic Summarization Methods g| 205 -- t| Dealing with Large Documents g| 206 -- g| Chapter 9 t| Managing Document Warehouse Metadata g| 209 -- t| Metadata Standards g| 210 -- t| Common Warehouse Model g| 212 -- t| Knowledge Management Based on the Open Information Model g| 221 -- t| Dublin Core g| 223 -- t| Adapting Metadata Standards to Document Warehousing g| 228 -- t| Content Metadata g| 228 -- t| Technical Metadata g| 230 -- t| Controlling Document Loads in the Warehouse g| 231 -- t| Prioritizing Items in Multiple Processing Queues g| 233 -- t| Summarizing Documents g| 234 -- t| Business Metadata g| 235 -- t| Quality: Timeliness and Reliability g| 236 -- t| Access Control g| 236 -- t| Versioning g| 237 -- g| Chapter 10 t| Ensuring Document Warehouse Integrity g| 239 -- t| Information Stewardship and Quality Control g| 240 -- t| Document Search and Retrieval g| 241 -- t| Text Analysis g| 248 -- t| Content Validation g| 253 -- t| Security g| 254 -- t| File System Security g| 255 -- t| Database Roles and Privileges g| 255 -- t| Programmatic Access Control g| 255 -- t| Virtual Database Security g| 256 -- t| Privacy g| 258 -- t| Contracts between Document Owners and the Warehouse g| 258 -- t| Is Privacy the Third Rail of Business Intelligence? Protecting Individuals and Organizations g| 259 -- g| Chapter 11 t| Choosing Tools for Building the Document Warehouse g| 261 -- t| Choosing Text Analysis Tools g| 262 -- t| Statistical/Heuristic Approach g| 264 -- t| Knowledge-Based Approach g| 275 -- t| Neural Network Approach: Megaputer's TextAnalyst g| 287 -- t| There Is More Than One Way to Mine Text g| 294 -- t| Choosing Supplemental Tools g| 298 -- t| Choosing Web Document Retrieval Tools g| 301 -- g| Chapter 12 t| Developing a Document Warehouse: A Checklist g| 305 -- g| Step 1 t| What Should Be Stored? g| 306 -- t| Understanding User Needs g| 306 -- t| Defining Document Sources g| 307 -- t| Metadata g| 308 -- t| User Profiles g| 309 -- t| Integration with the Data Warehouse g| 309 -- g| Step 2 t| Where Should It Be Stored? g| 310 -- g| Step 3 t| What Text Mining Services Should Be Used? g| 312 -- t| Indexing Services g| 313 -- t| Feature Extraction g| 314 -- t| Summarization g| 314 -- t| Document Clustering g| 314 -- t| Question Answering g| 315 -- t| Classification and Routing g| 315 -- t| Building Taxonomies and Thesauri g| 316 -- g| Step 4 t| How Should the Warehouse Be Populated? g| 316 -- t| Crawlers g| 317 -- t| Searching g| 318 -- g| Step 5 t| How Should the Warehouse Be Maintained? g| 319 -- g| Part 3 t| Text Mining g| 321 -- g| Chapter 13 t| What Is Text Mining? g| 323 -- t| Defining Text- Mining g| 324 -- t| Foundations of Text Mining g| 326 -- t| Information Retrieval g| 327 -- t| Computational Linguistics and Natural Language Processing g| 341 -- t| Discovering Knowledge in Text: Example Cases g| 351 -- t| Text Mining Methodology: Using the Cross-Industry Process Model for Data Mining g| 356 -- t| Business Understanding g| 358 -- t| Data Understanding g| 359 -- t| Data Preparation g| 360 -- t| Modeling g| 361 -- t| Evaluation g| 362 -- t| Deployment g| 362 -- t| Adopting the CRISP-DM to Text Mining g| 363 -- t| Text Mining Applications g| 365 -- t| Knowing Your Business g| 366 -- t| Knowing Your Customer g| 366 -- t| Knowing Your Competition and Market g| 367 -- g| Chapter 14 t| Know Thyself: Using Text Mining for Operational Management g| 369 -- t| Operations and Projects: Understanding the Distinct Needs of Each g| 371 -- t| Enterprise Document Management Systems g| 374 -- t| Benefits of Enterprise Document Management Systems g| 374 -- t| Limits of Enterprise Document Management Systems g| 375 -- t| Integrating Document Management with Document Warehousing g| 378 -- t| Document Extraction g| 378 -- t| Steps to Effective Text Mining for Operational Management g| 381 -- t| Specifying a Process for Extracting Information g| 381 -- t| Meeting Wide-Ranging Organizational Needs g| 384 -- g| Chapter 15 t| Knowing Your Business-to-Business Customer: Text Mining for Customer Relationship Management g| 387 -- t| Understanding Your Customer's Market g| 388 -- t| Developing a Customer Intelligence Profile g| 389 -- t| Sample Case of B-to-B Customer Relationship Management g| 391 -- t| Getting the Information I: Internal Sources g| 391 -- t| Getting the Information II: External Documents g| 397 -- t| Collecting External Documents g| 398 -- t| Preliminary Document Analysis g| 399 -- g| Chapter 16 t| Text Mining for Competitive Intelligence g| 407 -- t| Competitive Intelligence versus Business Intelligence g| 408 -- t| Competitive Intelligence Profiles g| 410 -- t| Identifying Information Sources g| 413 -- t| XML Text Processing Operations g| 416 -- t| XML Interface Models g| 417 --
    505
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    0
    t| Getting Financial Information from XBRL Documents g| 423 -- t| Practice of Competitive Intelligence g| 425 -- t| Competitive Intelligence in Health Care: Patent Analysis g| 426 -- t| Competitive Intelligence in Manufacturing: Financial Analysis g| 431 -- t| Competitive Intelligence in Financial Services Market: Market Issue Analysis g| 433 -- g| Chapter 17 t| Text Mining Tools g| 437 -- t| Criteria for Choosing Tools g| 438 -- t| Preprocessing Tools g| 438 -- t| Text Mining Tool Selection g| 446 -- t| Still Looking for a Silver Bullet: The Limits of Text Mining g| 453 -- t| Discourse Analysis g| 454 -- t| Semantic Models g| 455 -- g| Chapter 18 t| Changes in Business Intelligence g| 459 -- t| Business Intelligence and the Dynamics of Organizations g| 459 -- t| Changing Decision Makers g| 461 -- t| Changing Technologies g| 462 -- t| Changing Strategies g| 463 -- t| Meeting BI Needs with the Document Warehouse and Text Mining g| 466 -- t| Process of Document Warehousing g| 466 -- t| Text Mining for Decision Support g| 474 -- t| Text Mining and Operational Management g| 475 -- t| Text Mining and Customer Relationship Management g| 475 -- t| Text Mining and Competitive Intelligence g| 476 -- t| Shifting Emphasis of BI g| 478 -- t| Text, Not Just Numbers g| 478 -- t| Heuristics, Not Just Algorithms g| 478 -- t| Distributed Intelligence, Centralized Management g| 479 -- t| Next Steps: Where Do We Go from Here? g| 479 -- g| Appendix C t| Basic Document Warehouse Data Model g| 497.
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    a| Data warehousing.
    650
      
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    a| Data mining.
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    a| Text processing (Computer science)
    994
      
      
    a| Z0 b| VA@
    999
      
      
    a| QA76.9 .D37 S85 2001 w| LC i| X004475110 l| STACKS m| SCI-ENG t| BOOK-30DAY

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