Data Profiling:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing
2022
Cham Springer |
| Ausgabe: | Repr. of the original edition Morgan & Claypool, 2019 |
| Schriftenreihe: | Synthesis Lectures on Data Management
52 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 136 Seiten Illutrationen, Diagramme |
| ISBN: | 9783031007378 |
| ISSN: | 2153-5426 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | xi Contents Preface........................................................................................................................ xv Acknowledgments ................................................................................................. xvii 1 2 3 Discovering Metadata................................................................................................. 1 1.1 Motivation and Overview............................................................................................ 1 1.2 Data Profiling and Data Mining................................................................................. 3 1.3 Use Cases........................................................................................................................ 4 1.4 Organization of This Book............................................................................................ 6 Data Profiling Tasks..................................................................................................... 7 2.1 Single-Column Analysis .............................................................................................. 7 2.2 Dependency Discovery.................................................................................................. 9 2.3 Relaxed Dependencies.................................................................................................. 9 Single-Column Analysis........................................................................................... 11 3.1 4
Cardinalities..................................................................................................................11 3.2 Value Distributions....................................................................................................... 11 3.3 Data Types, Patterns, and Domains ......................................................................... 14 3.4 Data Completeness ................................................ 3.5 Approximate Statistics................................................................................................ 16 3.6 Summary and Discussion............................................................................................ 17 15 Dependency Discovery............................................................................................. 19 4.1 Dependency Definitions ............................................................................................ 19 4.1.1 Functional Dependencies..............................................................................21 4.1.2 Unique Column Combinations ................................................................... 22 4.1.3 Inclusion Dependencies ................................................................................ 23 4.2 Search 4.2.1 4.2.2 4.2.3 Space and Data Structures.............................................................................24 Lattices and Search Space Sizes................................................................... 24 Position List Indexes and Search Space Validation...................................27 Search
Complexity.........................................................................................29
xii 4.2.4 5 Null Semantics .............................................................................................. ЗО 4.3 Discovering Unique Column Combinations........................................................... 31 4.3.1 Gordian ....................................................................................................... 32 4.3.2 HCA................................................................................................................34 4.3.3 Duce............................................................................................................... 35 4.3.4 HyUCC ......................................................................................................... 37 4.3.5 Swan................................................................................................................38 4.4 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 ...................................................................................................46 4.4.6
FastFDs......................................................................................................... 48 4.4.7 Fdep ............................................................................................................... 50 4.4.8 HyFD ............................................................................................................. 51 4.5 Discovering Inclusion Dependencies........................................................................ 55 4.5.1 SQL-Based IND Validation......................................................................... 57 4.5.2 B B ................................................................................................................60 4.5.3 DeMarchi..................................................................................................... 61 4.5.4 Binder ........................................................................................................... 62 4.5.5 Spider ........................................................................................................... 64 4.5.6 S֊IndD........................................................................................................... 66 4.5.7 Sindy ..............................................................................................................68 4.5.8 Mind ............................................................................................................. 69 4.5.9 Find2 ............................................................................................................. 70 4.5.10
ZigZag............................................................................................................ 71 4.5.11 Mind2 ............................................................................................................ 72 Relaxed and Other Dependencies........................................................................... 75 5.1 Relaxing the Extent of a Dependency...................................................................... 75 5.1.1 Partial Dependencies...................................................................................... 76 5.1.2 Conditional Dependencies............................................................................ 76 5.2 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 5.3
xiii 5.4 6 Generalizing Functional Dependencies........................................................... 83 II 5.4.1 Denial Constraints .............................................................................. 5.4.2 Multivalued Dependencies.................................................................. 84 Use Cases......................................................... 6.1 6.2 6.3 6.4 6.5 Data Exploration............................................................................................ 87 I Schema Engineering ...................................................................................... 88 I Data Cleaning ................................................................................................ v ļ Query Optimization........................................................................................ 90 Data Integration.............................................................................................. ! 7 7.1 7.2 7.3 7.4 7.5 8 XML.................................................................................................................. 93 RDF................................................................................................................ Ί ime Series...................................................................................................... 94 Graphs ................................................................................................................. 95 Text.................................................................................................................... 9é
Research Prototypes......................................................................................... 97 Commercial Tools........................................................................................... Data Profiling Challenges...................................................................................... ^θ3 9.1 9.2 10 Profiling Non-Relational Data 93................... Data Profiling Tools................................................................................................... ^ 8.1 8.2 9 $7 Functional Challenges...................................................................................... 3θ3 9.1.1 Profiling Dynamic Data........................................................................ ^θ3 9.1.2 Interactive Profiling................................................................................3θ^ 9.1.3 Profiling for Integration............................................................................ 105 .9.1. 4 Interpreting Profiling Results......................................................... 3θ$ Non-Functional Challenges............................................................................ 9.2.1 Efficiency and Scalability...................................................................... 3 3^ 9.2.2 Profiling on New Architectures ............................................................. 9.2.3 Benchmarking Profiling Methods ........................................................ ^θ9 Conclusions
............................................................................................................... Ш Bibliography.............................................................................................................. Ü3 Authors’ Biographies................................................................................................135 ՜ ì ļ ( j
|
| adam_txt |
xi Contents Preface. xv Acknowledgments . xvii 1 2 3 Discovering Metadata. 1 1.1 Motivation and Overview. 1 1.2 Data Profiling and Data Mining. 3 1.3 Use Cases. 4 1.4 Organization of This Book. 6 Data Profiling Tasks. 7 2.1 Single-Column Analysis . 7 2.2 Dependency Discovery. 9 2.3 Relaxed Dependencies. 9 Single-Column Analysis. 11 3.1 4
Cardinalities.11 3.2 Value Distributions. 11 3.3 Data Types, Patterns, and Domains . 14 3.4 Data Completeness . 3.5 Approximate Statistics. 16 3.6 Summary and Discussion. 17 15 Dependency Discovery. 19 4.1 Dependency Definitions . 19 4.1.1 Functional Dependencies.21 4.1.2 Unique Column Combinations . 22 4.1.3 Inclusion Dependencies . 23 4.2 Search 4.2.1 4.2.2 4.2.3 Space and Data Structures.24 Lattices and Search Space Sizes. 24 Position List Indexes and Search Space Validation.27 Search
Complexity.29
xii 4.2.4 5 Null Semantics . ЗО 4.3 Discovering Unique Column Combinations. 31 4.3.1 Gordian . 32 4.3.2 HCA.34 4.3.3 Duce. 35 4.3.4 HyUCC . 37 4.3.5 Swan.38 4.4 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 .46 4.4.6
FastFDs. 48 4.4.7 Fdep . 50 4.4.8 HyFD . 51 4.5 Discovering Inclusion Dependencies. 55 4.5.1 SQL-Based IND Validation. 57 4.5.2 B B .60 4.5.3 DeMarchi. 61 4.5.4 Binder . 62 4.5.5 Spider . 64 4.5.6 S֊IndD. 66 4.5.7 Sindy .68 4.5.8 Mind . 69 4.5.9 Find2 . 70 4.5.10
ZigZag. 71 4.5.11 Mind2 . 72 Relaxed and Other Dependencies. 75 5.1 Relaxing the Extent of a Dependency. 75 5.1.1 Partial Dependencies. 76 5.1.2 Conditional Dependencies. 76 5.2 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 5.3
xiii 5.4 6 Generalizing Functional Dependencies. 83 II 5.4.1 Denial Constraints . 5.4.2 Multivalued Dependencies. 84 Use Cases. 6.1 6.2 6.3 6.4 6.5 Data Exploration. 87 I Schema Engineering . 88 I Data Cleaning . v ļ Query Optimization. 90 Data Integration. ! 7 7.1 7.2 7.3 7.4 7.5 8 XML. 93 RDF. Ί ime Series. 94 Graphs . 95 Text. 9é
Research Prototypes. 97 Commercial Tools. Data Profiling Challenges. ^θ3 9.1 9.2 10 Profiling Non-Relational Data 93. Data Profiling Tools. ^ 8.1 8.2 9 $7 Functional Challenges. 3θ3 9.1.1 Profiling Dynamic Data. ^θ3 9.1.2 Interactive Profiling.3θ^ 9.1.3 Profiling for Integration. 105 .9.1. 4 Interpreting Profiling Results. 3θ$ Non-Functional Challenges. 9.2.1 Efficiency and Scalability. 3 3^ 9.2.2 Profiling on New Architectures . 9.2.3 Benchmarking Profiling Methods . ^θ9 Conclusions
. Ш Bibliography. Ü3 Authors’ Biographies.135 ՜ ì ļ ( j |
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| illustrated | Illustrated |
| index_date | 2024-07-03T22:24:15Z |
| indexdate | 2024-07-10T09:54:08Z |
| institution | BVB |
| isbn | 9783031007378 |
| issn | 2153-5426 |
| language | English |
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| physical | XVIII, 136 Seiten Illutrationen, Diagramme |
| publishDate | 2022 |
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| series | Synthesis Lectures on Data Management |
| series2 | Synthesis Lectures on Data Management |
| spelling | Abedjan, Ziawasch Verfasser (DE-588)1081174676 aut Data Profiling by Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock Repr. of the original edition Morgan & Claypool, 2019 Cham Springer International Publishing 2022 Cham Springer XVIII, 136 Seiten Illutrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Synthesis Lectures on Data Management 52 2153-5426 Computer Communication Networks Data Structures and Information Theory Computer networks Data structures (Computer science) Information theory Data-Profiling (DE-588)7670125-6 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Metadaten (DE-588)4410512-5 gnd rswk-swf Data-Profiling (DE-588)7670125-6 s Data Mining (DE-588)4428654-5 s Metadaten (DE-588)4410512-5 s DE-604 Golab, Lukasz Sonstige (DE-588)1207414689 oth Naumann, Felix 1971- Sonstige (DE-588)129576379 oth Papenbrock, Thorsten Sonstige (DE-588)1153740621 oth Erscheint auch als Druck-Ausgabe 978-3-031-00092-8 Erscheint auch als Online-Ausgabe 978-3-031-01865-7 Synthesis Lectures on Data Management 52 (DE-604)BV036766043 52 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034323288&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Abedjan, Ziawasch Data Profiling Synthesis Lectures on Data Management Computer Communication Networks Data Structures and Information Theory Computer networks Data structures (Computer science) Information theory Data-Profiling (DE-588)7670125-6 gnd Data Mining (DE-588)4428654-5 gnd Metadaten (DE-588)4410512-5 gnd |
| subject_GND | (DE-588)7670125-6 (DE-588)4428654-5 (DE-588)4410512-5 |
| title | Data Profiling |
| title_auth | Data Profiling |
| title_exact_search | Data Profiling |
| title_exact_search_txtP | Data Profiling |
| title_full | Data Profiling by Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock |
| title_fullStr | Data Profiling by Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock |
| title_full_unstemmed | Data Profiling by Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock |
| title_short | Data Profiling |
| title_sort | data profiling |
| topic | Computer Communication Networks Data Structures and Information Theory Computer networks Data structures (Computer science) Information theory Data-Profiling (DE-588)7670125-6 gnd Data Mining (DE-588)4428654-5 gnd Metadaten (DE-588)4410512-5 gnd |
| topic_facet | Computer Communication Networks Data Structures and Information Theory Computer networks Data structures (Computer science) Information theory Data-Profiling Data Mining Metadaten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034323288&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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