| LDR | | 02575nam u200445 4500 |
| 001 | | 000000418691 |
| 005 | | 20190215163053 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438371583 |
| 035 | |
▼a (MiAaPQ)AAI10845463 |
| 035 | |
▼a (MiAaPQ)purdue:23258 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Rezig, Elkindi. |
| 245 | 10 |
▼a Online Data Cleaning. |
| 260 | |
▼a [S.l.]:
▼b Purdue University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 140 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Advisers: Walid G. Aref |
| 502 | 1 |
▼a Thesis (Ph.D.)--Purdue University, 2018. |
| 520 | |
▼a Data-centric applications have never been more ubiquitous in our lives, e.g., search engines, route navigation and social media. This has brought along a new age where digital data is at the core of many decisions we make as individuals, e.g., l |
| 520 | |
▼a Dirty data is the product of many factors which include data entry errors and integration of several data sources. Data integration of multiple sources is especially prone to producing dirty data. For instance, while individual sources may not h |
| 520 | |
▼a There is a wide spectrum of errors that can be found in the data, e,g, duplicate records, missing values, obsolete data, etc. To address these problems, several data cleaning efforts have been proposed, e.g., record linkage to identify duplicate |
| 520 | |
▼a We first present a framework that supports online record linkage and fusion over Web databases. Our system processes queries posted to Web databases. Query results are deduplicated, fused and then stored in a cache for future reference. The cach |
| 520 | |
▼a To address integrity constraints violations, we propose a novel way to approach Functional Dependency repairs, develop a new class of repairs and then demonstrate it is superior to existing efforts, in runtime and accuracy. We then show how our |
| 590 | |
▼a School code: 0183. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Engineering. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0537 |
| 710 | 20 |
▼a Purdue University.
▼b Computer Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0183 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000062
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |