Managing probabilistic duplicates in databases / Mohamed Mahmoud Hafez Mahmoud Abdelrahman ; Supervised Osman Hegazy Mohamed , Hamid Elbastawissy

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Mohamed Mahmoud Hafez Mahmoud Abdelrahman

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TextLanguage: English Publication details: Cairo : Mohamed Mahmoud Hafez Mahmoud Abdelrahman , 2014Description: 69 Leaves ; 30cmOther title:

إدارة التكرارات الاحتمالية فى قواعد البيانات [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems Summary: Data fusion in the virtual data integration environment starts after detecting and clustering duplicated records from the different integrated data sources. It refers to the process of selecting from attribute values in the clustered records, an attribute value to form a single record representing the real world object. Many trials were done to solve the inconsistencies at the data level, but all of them didn{u2019}t perform the data fusion process in full automation without any predefined metadata or any user intervention. In this thesis, a new branch is opened to do data fusion in a fully-automated process and two data fusion techniques are proposed. The proposed data dependency (DD) technique solves conflicts using some final statistical scores for each requested attribute based on two scores

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.04.M.Sc.2014.Mo.M (Browse shelf(Opens below))		Not for loan	01010110064562000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.04.M.Sc.2014.Mo.M (Browse shelf(Opens below))	64562.CD	Not for loan	01020110064562000

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Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems

Data fusion in the virtual data integration environment starts after detecting and clustering duplicated records from the different integrated data sources. It refers to the process of selecting from attribute values in the clustered records, an attribute value to form a single record representing the real world object. Many trials were done to solve the inconsistencies at the data level, but all of them didn{u2019}t perform the data fusion process in full automation without any predefined metadata or any user intervention. In this thesis, a new branch is opened to do data fusion in a fully-automated process and two data fusion techniques are proposed. The proposed data dependency (DD) technique solves conflicts using some final statistical scores for each requested attribute based on two scores

Issued also as CD

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