| 000 | 03506nam a22005055i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01835-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240730164114.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 |
_a9783031018350 _9978-3-031-01835-0 |
||
| 024 | 7 |
_a10.1007/978-3-031-01835-0 _2doi |
|
| 050 | 4 | _aTK5105.5-5105.9 | |
| 072 | 7 |
_aUKN _2bicssc |
|
| 072 | 7 |
_aCOM043000 _2bisacsh |
|
| 072 | 7 |
_aUKN _2thema |
|
| 082 | 0 | 4 |
_a004.6 _223 |
| 100 | 1 |
_aNauman, Felix. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982152 |
|
| 245 | 1 | 3 |
_aAn Introduction to Duplicate Detection _h[electronic resource] / _cby Felix Nauman, Melanie Herschel. |
| 250 | _a1st ed. 2010. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010. |
|
| 300 |
_aIX, 77 p. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 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 | _aData Cleansing: Introduction and Motivation -- Problem Definition -- Similarity Functions -- Duplicate Detection Algorithms -- Evaluating Detection Success -- Conclusion and Outlook -- Bibliography. | |
| 520 | _aWith the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally, we discuss methods to evaluate the success of duplicate detection. Table of Contents: Data Cleansing: Introduction and Motivation / Problem Definition / Similarity Functions / Duplicate Detection Algorithms / Evaluating Detection Success / Conclusion and Outlook / Bibliography. | ||
| 650 | 0 |
_aComputer networks . _931572 |
|
| 650 | 0 |
_aData structures (Computer science). _98188 |
|
| 650 | 0 |
_aInformation theory. _914256 |
|
| 650 | 1 | 4 |
_aComputer Communication Networks. _982153 |
| 650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
| 700 | 1 |
_aHerschel, Melanie. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982154 |
|
| 710 | 2 |
_aSpringerLink (Online service) _982155 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031007071 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031029639 |
| 830 | 0 |
_aSynthesis Lectures on Data Management, _x2153-5426 _982156 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01835-0 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85305 _d85305 |
||