Principles of data integration:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam ; Boston ; Heidelberg ; London
Morgan Kaufmann
[2012]
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025248536&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025248536&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xviii, 497 Seiten Illustrationen |
| ISBN: | 9780124160446 |
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| adam_text | Titel: Principles of data integration
Autor: Doan, AnHai
Jahr: 2012
Contents
Preface xvii
1. Introduction 1
1.1 What Is Data Integration? 1
1.2 Why Is It Hard? 6
1.2.1 Systems Reasons 6
1.2.2 Logical Reasons 7
1.2.3 Social and Administrative Reasons 8
1.2.4 Setting Expectations 9
1.3 Data Integration Architectures 9
1.3.1 Components of the Data Integration System 10
1.3.2 Example Data Integration Scenario 12
1.4 Outline of the Book 15
Bibliographic Notes 17
Parti
Foundational Data Integration Techniques 19
2. Manipulating Query Expressions 21
2.1 Review of Database Concepts 22
2.1.1 Data Model 22
2.1.2 Integrity Constraints 24
2.1.3 Queries and Answers 25
2.1.4 Conjunctive Queries 26
2.1.5 Datalog Programs 28
2.2 Query Unfolding 29
vu
viii Contents
2.3 Query Containment and Equivalence 31
2.3.1 Formal Definition 31
2.3.2 Containment of Conjunctive Queries 32
2.3.3 Unions of Conjunctive Queries 34
2.3.4 Conjunctive Queries with Interpreted Predicates 35
2.3.5 Conjunctive Queries with Negation 37
2.3.6 Bag Semantics, Grouping, and Aggregation 41
2.4 Answering Queries Using Views 43
2.4.1 Problem Definition 44
2.4.2 When Is a View Relevant to a Query? 46
2.4.3 The Possible Length of a Rewriting 47
2.4.4 The Bucket and MiniCon Algorithms 48
2.4.5 A Logical Approach: The Inverse-Rules Algorithm 56
2.4.6 Comparison of the Algorithms 57
2.4.7 View-Based Query Answering 58
Bibliographic Notes 62
3. Describing Data Sources 65
3.1 Overview and Desiderata 65
3.2 Schema Mapping Languages 68
3.2.1 Principles of Schema Mapping Languages 69
3.2.2 Global-as-View 70
3.2.3 Local-as-View 73
3.2.4 Global-and-Local-as-View 77
3.2.5 Tuple-Generating Dependencies 78
3.3 Access-Pattern Limitations 80
3.3.1 Modeling Access-Pattern Limitations 81
3.3.2 Generating Executable Plans 81
3.4 Integrity Constraints on the Mediated Schema 85
3.4.1 LAV with Integrity Constraints 85
3.4.2 GAV with Integrity Constraints 88
3.5 Answer Completeness 89
3.5.1 Local Completeness 89
3.5.2 Detecting Answer Completeness 90
3.6 Data-Level Heterogeneity 92
3.6.1 Differences of Scale 92
3.6.2 Multiple References to the Same Entity 93
Bibliographic Notes 93
Contents ix
4. String Matching 95
4.1 Problem Description 95
4.2 Similarity Measures 96
4.2.1 Sequence-Based Similarity Measures 96
4.2.2 Set-Based Similarity Measures 104
4.2.3 Hybrid Similarity Measures 106
4.2.4 Phonetic Similarity Measures 109
4.3 Scaling Up String Matching 110
4.3.1 Inverted Index Over Strings 111
4.3.2 Size Filtering 112
4.3.3 Prefix Filtering 113
4.3.4 Position Filtering 115
4.3.5 Bound Filtering 116
4.3.6 Extending Scaling Techniques to Other Similarity
Measures 117
Bibliographic Notes 118
5. Schema Matching and Mapping 121
5.1 Problem Definition 121
5.1.1 Semantic Mappings 122
5.1.2 Semantic Matches 123
5.1.3 Schema Matching and Mapping 124
5.2 Challenges of Schema Matching and Mapping 124
5.3 Overview of Matching and Mapping Systems 127
5.3.1 Schema Matching Systems 127
5.3.2 Schema Mapping Systems 129
5.4 Matchers 129
5.4.1 Name-Based Matchers 130
5.4.2 Instance-Based Matchers 132
5.5 Combining Match Predictions 134
5.6 Enforcing Domain Integrity Constraints 135
5.6.1 Domain Integrity Constraints 136
5.6.2 Searching the Space of Match Combinations 137
5.7 Match Selector 143
5.8 Reusing Previous Matches 145
5.8.1 Learning to Match 145
5.8.2 Learners 147
5.8.3 Training the Meta-Learner 149
x Contents
5.9 Many-to-Many Matches 150
5.10 From Matches to Mappings 152
Bibliographic Notes 159
6. General Schema Manipulation Operators 161
6.1 Model Management Operators 162
6.2 Merge 164
6.3 ModelGen 166
6.4 Invert 168
6.5 Toward Model Management Systems 170
Bibliographic Notes 170
7. Data Matching 173
7.1 Problem Definition 173
7.2 Rule-Based Matching 175
7.3 Learning-Based Matching 177
7.4 Matching by Clustering 180
7.5 Probabilistic Approaches to Data Matching 182
7.5.1 Bayesian Networks 183
7.5.2 Data Matching with Naive Bayes 190
7.5.3 Modeling Feature Correlations 192
7.5.4 Matching Mentions of Entities in Text 193
7.6 Collective Matching 198
7.6.1 Collective Matching Based on Clustering 200
7.6.2 Collectively Matching Entity Mentions in Documents 201
7.7 Scaling Up Data Matching 203
7.7.1 Scaling Up Rule-Based Matching 203
7.7.2 Scaling Up Other Matching Methods 204
Bibliographic Notes 205
8. Query Processing 209
8.1 Background: DBMS Query Processing 210
8.1.1 Choosing a Query Execution Plan 211
8.1.2 Executing a Query Plan 214
Contents xi
8.2 Background: Distributed Query Processing 216
8.2.1 Data Placement and Shipment 217
8.2.2 loining in Two Phases 218
8.3 Query Processing for Data Integration 219
8.4 Generating Initial Query Plans 221
8.5 Query Execution for Internet Data 222
8.5.1 Multithreaded, Pipelined, Dataflow Architecture 222
8.5.2 Interfacing with Autonomous Sources 223
8.5.3 Handling Failure 225
8.6 Overview of Adaptive Query Processing 225
8.7 Event-Driven Adaptivity 226
8.7.1 Handling Source Failures and Delays 227
8.7.2 Handling Unexpected Cardinalities at Pipeline End 228
8.8 Performance-Driven Adaptivity 231
8.8.1 Eddies: Queueing-Based Plan Selection 232
8.8.2 Corrective Query Processing: Cost-Based
Reoptimization 235
Bibliographic Notes 240
9. Wrappers 243
9.1 Introduction 243
9.1.1 The Wrapper Construction Problem 244
9.1.2 Challenges of Wrapper Construction 245
9.1.3 Categories of Solutions 246
9.2 Manual Wrapper Construction 247
9.3 Learning-Based Wrapper Construction 249
9.3.1 HLRT Wrappers 249
9.3.2 Stalker Wrappers 251
9.4 Wrapper Learning without Schema 256
9.4.1 Modeling Schema T$ and Program Ew 257
9.4.2 Inferring Schema Ts and Program Ew 258
9.5 Interactive Wrapper Construction 263
9.5.1 Interactive Labeling of Pages with Stalker 263
9.5.2 Identifying Correct Extraction Results with Poly 264
9.5.3 Creating Extraction Rules with Lixto 267
Bibliographic Notes 270
xii Contents
10. Data Warehousing and Caching 271
10.1 Data Warehousing 272
10.1.1 Data Warehouse Design 274
10.1.2 ETL: Extract/Transform/Load 275
10.2 Data Exchange: Declarative Warehousing 276
10.2.1 Data-Exchange Settings 277
10.2.2 Data-Exchange Solutions 278
10.2.3 Universal Solutions 279
10.2.4 Core Universal Solutions 281
10.2.5 Querying the Materialized Repository 283
10.3 Caching and Partial Materialization 283
10.4 Direct Analysis of Local, External Data 284
Bibliographic Notes 287
Part II
Integration with Extended Data Representations 289
11. XML 291
11.1 Data Model 292
11.2 XML Structural and Schema Definitions 296
11.2.1 Document Type Definitions (DTDs) 296
11.2.2 XML Schema (XSD) 298
11.3 Query Language 300
11.3.1 Precursors: DOM and SAX 300
11.3.2 XPath: A Primitive for XML Querying 301
11.3.3 XQuery: Query Capabilities for XML 306
11.4 Query Processing for XML 312
11.4.1 XML Path Matching 313
11.4.2 XML Output 317
11.4.3 XML Query Optimization 317
11.5 Schema Mapping for XML 317
11.5.1 Mappings with Nesting 318
11.5.2 Query Reformulation with Nested
Mappings 321
Bibliographic Notes 322
Contents xiii
12. Ontologies and Knowledge Representation 325
12.1 Example: Using KR in Data Integration 326
12.2 Description Logics 327
12.2.1 Syntax of Description Logics 328
12.2.2 Semantics of Description Logics 329
12.2.3 Inference in Description Logics 331
12.2.4 Comparing Description Logics to Database Reasoning 333
12.3 The Semantic Web 335
12.3.1 Resource Description Framework 336
12.3.2 RDF Schema 340
12.3.3 Web Ontology Language (OWL) 341
12.3.4 Querying RDF: The SPARQL Language 342
Bibliographic Notes 343
13. Incorporating Uncertainty into Data Integration 345
13.1 Representing Uncertainty 346
13.1.1 Probabilistic Data Representations 348
13.1.2 From Uncertainty to Probabilities 350
13.2 Modeling Uncertain Schema Mappings 350
13.2.1 Probabilistic Mappings 351
13.2.2 Semantics of Probabilistic Mappings 352
13.2.3 By-Table Semantics 353
13.2.4 By-Tuple Semantics 354
13.3 Uncertainty and Data Provenance 356
Bibliographic Notes 356
14. Data Provenance 359
14.1 The Two Views of Provenance 360
14.1.1 Provenance as Annotations on Data 360
14.1.2 Provenance as a Graph of Data Relationships 361
14.1.3 Interchangeability of the Two Views 362
14.2 Applications of Data Provenance 362
14.3 Provenance Semirings 363
14.3.1 The Semiring Formal Model 364
14.3.2 Applications of the Semiring Model 366
14.4 Storing Provenance 368
Bibliographic Notes 370
xiv Contents
Part III
Novel Integration Architectures 373
15. Data Integration on the Web 375
15.1 What Can We Do with Web Data? 377
15.2 The Deep Web 379
15.2.1 Vertical Search 380
15.2.2 Surfacing the Deep Web 383
15.3 Topical Portals 385
15.4 Lightweight Combination of Web Data 388
15.4.1 Discovering Structured Data on the Web 391
15.4.2 Importing Data 391
15.4.3 Combining Multiple Data Sets 393
15.4.4 Reusing the Work of Others 393
15.5 Pay-as-You-Go Data Management 394
Bibliographic Notes 395
16. Keyword Search: Integration on Demand 399
16.1 Keyword Search over Structured Data 399
16.1.1 Data Graph 400
16.1.2 Keyword Matching and Scoring Models 401
16.2 Computing Ranked Results 403
16.2.1 Graph Expansion Algorithms 403
16.2.2 Threshold-Based Merging 404
16.3 Keyword Search for Data Integration 407
16.3.1 Scalable Automatic Edge Inference 407
16.3.2 Scalable Query Answering 409
16.3.3 Learning to Adjust Weights on Edges and Nodes 409
Bibliographic Notes 410
17. Peer-to-Peer Integration 413
17.1 Peers and Mappings 414
17.2 Semantics of Mappings 418
17.3 Complexity of Query Answering in PDMS 419
17.3.1 Cyclic PDMS 420
17.3.2 Interpreted Predicates in Peer Mappings 421
Contents xv
17.4 Query Reformulation Algorithm 421
17.5 Composing Mappings 426
17.6 Peer Data Management with Looser Mappings 429
17.6.1 Similarity-Based Mappings 429
17.6.2 Mapping Tables 430
Bibliographic Notes 432
18. Integration in Support of Collaboration 435
18.1 What Makes Collaboration Different 435
18.2 Processing Corrections and Feedback 436
18.2.1 Direct User Updates Propagated Downstream 437
18.2.2 Feedback or Updates Propagated Back Upstream 437
18.3 Collaborative Annotation and Presentation 438
18.3.1 Mappings as Annotations: Trails 438
18.3.2 Comments and Discussions as Annotations 439
18.4 Dynamic Data: Collaborative Data Sharing 441
18.4.1 Basic Architecture 443
18.4.2 Mapping Updates and Materializing Instances 444
18.4.3 Reconciling Conflicts 449
Bibliographic Notes 450
19. The Future of Data Integration 453
19.1 Uncertainty, Provenance, and Cleaning 453
19.2 Crowdsourcing and Human Computing 454
19.3 Building Large-Scale Structured Web Databases 454
19.4 Lightweight Integration 455
19.5 Visualizing Integrated Data 456
19.6 Integrating Social Media 456
19.7 Cluster- and Cloud-Based Parallel Processing
and Caching 457
Bibliography 459
Index 487
|
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| illustrated | Illustrated |
| indexdate | 2024-12-20T16:14:07Z |
| institution | BVB |
| isbn | 9780124160446 |
| language | English |
| lccn | 2012003883 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025248536 |
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| physical | xviii, 497 Seiten Illustrationen |
| publishDate | 2012 |
| publishDateSearch | 2012 |
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| publisher | Morgan Kaufmann |
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| spellingShingle | Doan, AnHai Halevy, Alon Ives, Zachary G. Principles of data integration Wissensrepräsentation (DE-588)4049534-6 gnd Semantic Web (DE-588)4688372-1 gnd Softwarearchitektur (DE-588)4121677-5 gnd Datenintegration (DE-588)4197730-0 gnd |
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| title | Principles of data integration |
| title_auth | Principles of data integration |
| title_exact_search | Principles of data integration |
| title_full | Principles of data integration AnHai Doan, Alon Halevy, Zachary Ives |
| title_fullStr | Principles of data integration AnHai Doan, Alon Halevy, Zachary Ives |
| title_full_unstemmed | Principles of data integration AnHai Doan, Alon Halevy, Zachary Ives |
| title_short | Principles of data integration |
| title_sort | principles of data integration |
| topic | Wissensrepräsentation (DE-588)4049534-6 gnd Semantic Web (DE-588)4688372-1 gnd Softwarearchitektur (DE-588)4121677-5 gnd Datenintegration (DE-588)4197730-0 gnd |
| topic_facet | Wissensrepräsentation Semantic Web Softwarearchitektur Datenintegration |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025248536&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025248536&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT doananhai principlesofdataintegration AT halevyalon principlesofdataintegration AT iveszacharyg principlesofdataintegration |
Inhaltsverzeichnis
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Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 DAT 654f 2012 A 8053 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |