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Bibliographic Details
Main Authors:	Abedjan, Ziawasch (Author), Golab, Lukasz (Author), Naumann, Felix 1971- (Author), Papenbrock, Thorsten (Author)
Format:	Book
Language:	English
Published:	[San Rafael, California] Morgan & Claypool Publishers [2019]
Series:	Synthesis lectures on data management #52
Subjects:	Metadaten Data Mining Data-Profiling
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030738692&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	xviii, 136 Seiten Illustrationen, Diagramme
ISBN:	9781681734460 9781681734484

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Record in the Search Index

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adam_text	xi Contents Preface............................ Acknowledgments 1 2 xvii 1 1 1.1 Motivation and Overview...................... 1.2 Data Profiling and Data Mining.......... ............................... Use Cases............... ............................. 1.4 Organization of This Book. ........................................... Data Profiling Tasks ................... 3 ....... 4 1.3 6 ........................ 7 Single-Column Analysis .................................................... 7 2.2 Dependency Discovery.............................................................................. 9 2.3 Relaxed Dependencies................................. 9 Single-Column Analysis .......................................... 11 3.1 Cardinalities..................... 11 3.2 Value Distributions................................................... 11 3.3 Data Types, Patterns, and Domains.......... ............................................ 14 3.4 Data Completeness ................................................................................... 15 3.5 3.6 4 ................................................. Discovering Metadata................................................... 2.1 3 .xv Approximate Statistics............... Summary and Discussion........................................ Dependency Discovery ................................. 16 17 19 4.1 Dependency Definitions .......................................... 4.1.1 Functional Dependencies ..................................... ............. 4.1.2 Unique Column Combinations ................. . 4.1.3 Inclusion Dependencies ........................ 19 21 22 23 4.2 Search Space and Data Structures ....................................... 4.2.1 Lattices and Search Space Sizes............................. 4.2.2 Position List Indexes and Search Space Validation ................ 4.2.3 Search Complexity ...................................................... 24 24 27 29 ii 4.2.4 4.3 4.4 Gordian 32 .... ......................................... 4.3.2 HCA............................................................. 34 4.3.3 Duce.................................................. 35 4.3.4 HyUCC 37 4.3.5 Swan................ ................................... 38 Discovering Functional Dependencies ................ 39 4.4.1 Tane ... ....... 41 4.4.2 Fun ... ....... 42 4.4.3 FD_.Mine................. 45 4.4.4 Dfd....... . ..................................................... 45 4.4.5 Dep-Miner 4.4.6 FastFDs...... . .................... 4.4.7 Fdep .................... 46 48 50 ....... HyFD................................... ......... 51 Discovering Inclusion Dependencies........................... 4.5.1 5 ЗО Discovering Unique Column Combinations................................ 31 4.3.1 4.4.8 4.5 Null Semantics ............. SQL-Based IND Validation................................ ......... 57 5.2 5.3 60 4.5.2 B B 4.5.3 DeMarchi............. 61 4.5.4 Binder .................... 62 4.5.5 Spider 64 4.5.6 S-IndD...... . .............. 4.5.7 Sindy 4.5.8 4.5.9 ..... ..... ..... 66 .......................... 68 .Mind . ...... 69 Find2 ..... ........ 70 4.5,10 ZigZag....... . ............ 71 4.5.11 Mind2 72 ............................. Relaxed and Other Dependencies . ............... 5.1 55 Relaxing the Extent of a Dependency...... ..................... 75 75 5.1.1 Partial Dependencies.............. . ................. . .......... .. 76 5.1.2 Conditional Dependencies.......... 76 Relaxing Attribute Comparisons ................ 78 5.2.1 Metric and Matching Dependencies........................ 78 5.2.2 Order and Sequential Dependencies .......... 81 Approximating the Dependency Discovery....... . .......... 83 xiii 5.4 6 7 Use Cases .............. 9 Ю .87 6.1 Data Exploration .................... 87 6.2 Schema Engineering .......................................................... 88 6.3 Data Cleaning ............................................... 89 6.4 Query Optimization ............................ 90 6.5 Data integration ............... 91 Profiling Non - Relational Data ................................. 7.1 8 Generalizing Funcționa! Dependencies............... ...................................... 83 5.4.1 Denial Constraints....................... 84 5.4.2 Multivalued Dependencies....................................................................... . 84 .93 XML..................... 93 7.2 RDF........... .................... 94 7.3 Time Series............... ................................................ 94 7.4 Graphs ..................................... 95 7.5 Text....................................................... 96 Data Profiling Tools........ ............................ 97 8.1 Research Prototypes..................................................... 97 8.2 Commercial Cools .................................. 99 Data Profiling Challenges.................... ....... ................... .................... 103 9.1 Functional Challenges..................................................... 9.1.1 Profiling Dynamic Data........... ................. 9.1.2 Interactive Profiling............................................ 9.1,3 Profiling for Integration.............................. 9.1.4 Interpreting Profiling Results................... 103 103 104 105 106 9.2 Non- Functional Challenges ........................ 9.2.1 Efficiency and Scalability ................................................................... 9.2.2 Profiling on New Architectures ............ ........................ 9.2.3 Benchmarking Profiling Methods ............. ................ 108 108 109 109 Conclusions ................................................... Bibliography ...................... Authors ’ Biograph։ es Ill
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language	English
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physical	xviii, 136 Seiten Illustrationen, Diagramme
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series	Synthesis lectures on data management
series2	Synthesis lectures on data management
spellingShingle	Abedjan, Ziawasch Golab, Lukasz Naumann, Felix 1971- Papenbrock, Thorsten Data profiling Synthesis lectures on data management Metadaten (DE-588)4410512-5 gnd Data Mining (DE-588)4428654-5 gnd Data-Profiling (DE-588)7670125-6 gnd
subject_GND	(DE-588)4410512-5 (DE-588)4428654-5 (DE-588)7670125-6
title	Data profiling
title_auth	Data profiling
title_exact_search	Data profiling
title_full	Data profiling Ziawasch Abedjan (Technische Universität Berlin), Lukasz Golab (University of Waterloo), Felix Naumann (Hasso Plattner Institute, University of Potsdam), Thorsten Papenbrock (Hasso Plattner Institute, University of Potsdam)
title_fullStr	Data profiling Ziawasch Abedjan (Technische Universität Berlin), Lukasz Golab (University of Waterloo), Felix Naumann (Hasso Plattner Institute, University of Potsdam), Thorsten Papenbrock (Hasso Plattner Institute, University of Potsdam)
title_full_unstemmed	Data profiling Ziawasch Abedjan (Technische Universität Berlin), Lukasz Golab (University of Waterloo), Felix Naumann (Hasso Plattner Institute, University of Potsdam), Thorsten Papenbrock (Hasso Plattner Institute, University of Potsdam)
title_short	Data profiling
title_sort	data profiling
topic	Metadaten (DE-588)4410512-5 gnd Data Mining (DE-588)4428654-5 gnd Data-Profiling (DE-588)7670125-6 gnd
topic_facet	Metadaten Data Mining Data-Profiling
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030738692&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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work_keys_str_mv	AT abedjanziawasch dataprofiling AT golablukasz dataprofiling AT naumannfelix dataprofiling AT papenbrockthorsten dataprofiling

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