000 | 03807nam a22005415i 4500 | ||
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001 | 978-3-031-01865-7 | ||
003 | DE-He213 | ||
005 | 20240730163438.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2019 sz | s |||| 0|eng d | ||
020 |
_a9783031018657 _9978-3-031-01865-7 |
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024 | 7 |
_a10.1007/978-3-031-01865-7 _2doi |
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050 | 4 | _aTK5105.5-5105.9 | |
072 | 7 |
_aUKN _2bicssc |
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_aCOM043000 _2bisacsh |
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_aUKN _2thema |
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_a004.6 _223 |
100 | 1 |
_aAbedjan, Ziawasch. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978573 |
|
245 | 1 | 0 |
_aData Profiling _h[electronic resource] / _cby Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
300 |
_aXV, 136 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSynthesis Lectures on Data Management, _x2153-5426 |
|
505 | 0 | _aPreface -- Acknowledgments -- Discovering Metadata -- Data Profiling Tasks -- Single-Column Analysis -- Dependency Discovery -- Relaxed and Other Dependencies -- Use Cases -- Profiling Non-Relational Data -- Data Profiling Tools -- Data Profiling Challenges -- Conclusions -- Bibliography -- Authors' Biographies . | |
520 | _aData profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks,and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area. | ||
650 | 0 |
_aComputer networks . _931572 |
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650 | 0 |
_aData structures (Computer science). _98188 |
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650 | 0 |
_aInformation theory. _914256 |
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650 | 1 | 4 |
_aComputer Communication Networks. _978574 |
650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
700 | 1 |
_aGolab, Lukasz. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978575 |
|
700 | 1 |
_aNaumann, Felix. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978576 |
|
700 | 1 |
_aPapenbrock, Thorsten. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978577 |
|
710 | 2 |
_aSpringerLink (Online service) _978578 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031000928 |
776 | 0 | 8 |
_iPrinted edition: _z9783031007378 |
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_iPrinted edition: _z9783031029936 |
830 | 0 |
_aSynthesis Lectures on Data Management, _x2153-5426 _978579 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01865-7 |
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