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Online Data Cleaning

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자료유형학위논문
서명/저자사항Online Data Cleaning.
개인저자Rezig, Elkindi.
단체저자명Purdue University. Computer Sciences.
발행사항[S.l.]: Purdue University., 2018.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2018.
형태사항140 p.
기본자료 저록Dissertation Abstracts International 80-02B(E).
Dissertation Abstract International
ISBN9780438371583
학위논문주기Thesis (Ph.D.)--Purdue University, 2018.
일반주기 Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Advisers: Walid G. Aref
요약Data-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
요약Dirty 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
요약There 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
요약We 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
요약To 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
일반주제명Computer science.
Engineering.
언어영어
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