Missing values estimation for skylines in incomplete database

Incompleteness of data is a common problem in many databases including web heterogeneous databases, multi-relational databases, spatial and temporal databases and data integration. The incompleteness of data introduces challenges in processing queries as providing accurate results that best meet the...

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Main Authors: Aljuboori, Ali A.Alwan, Ibrahim, Hamidah, Udzir, Nur Izura, Sidi, Fatimah
Format: Article
Language:English
English
English
Published: Zarqa University, Jordan 2018
Subjects:
Online Access:http://irep.iium.edu.my/47989/
http://irep.iium.edu.my/47989/
http://irep.iium.edu.my/47989/1/47989_Missing%20values%20estimation%20for%20skylines.pdf
http://irep.iium.edu.my/47989/2/47989_Missing%20values%20estimation%20for%20skylines_SCOPUS.pdf
http://irep.iium.edu.my/47989/3/47989_Missing%20values%20estimation%20for%20skylines_WOS.pdf
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recordtype eprints
spelling iium-479892018-05-25T02:08:07Z http://irep.iium.edu.my/47989/ Missing values estimation for skylines in incomplete database Aljuboori, Ali A.Alwan Ibrahim, Hamidah Udzir, Nur Izura Sidi, Fatimah QA76 Computer software Incompleteness of data is a common problem in many databases including web heterogeneous databases, multi-relational databases, spatial and temporal databases and data integration. The incompleteness of data introduces challenges in processing queries as providing accurate results that best meet the query conditions over incomplete database is not a trivial task. Several techniques have been proposed to process queries in incomplete database. Some of these techniques retrieve the query results based on the existing values rather than estimating the missing values. Such techniques are undesirable in many cases as the dimensions with missing values might be the important dimensions of the user’s query. Besides, the output is incomplete and might not satisfy the user preferences. In this paper we propose an approach that estimates missing values in skylines to guide users in selecting the most appropriate skylines from the several candidate skylines. The approach utilizes the concept of mining attribute correlations to generate an Approximate Functional Dependencies (AFDs) that captured the relationships between the dimensions. Besides, identifying the strength of probability correlations to estimate the values. Then, the skylines with estimated values are ranked. By doing so, we ensure that the retrieved skylines are in the order of their estimated precision. Zarqa University, Jordan 2018-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/47989/1/47989_Missing%20values%20estimation%20for%20skylines.pdf application/pdf en http://irep.iium.edu.my/47989/2/47989_Missing%20values%20estimation%20for%20skylines_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/47989/3/47989_Missing%20values%20estimation%20for%20skylines_WOS.pdf Aljuboori, Ali A.Alwan and Ibrahim, Hamidah and Udzir, Nur Izura and Sidi, Fatimah (2018) Missing values estimation for skylines in incomplete database. The International Arab Journal of Information Technology, 15 (1). pp. 66-75. ISSN 1683-3198 http://iajit.org/PDF/January%202018,%20No.%201/11178.pdf
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Aljuboori, Ali A.Alwan
Ibrahim, Hamidah
Udzir, Nur Izura
Sidi, Fatimah
Missing values estimation for skylines in incomplete database
description Incompleteness of data is a common problem in many databases including web heterogeneous databases, multi-relational databases, spatial and temporal databases and data integration. The incompleteness of data introduces challenges in processing queries as providing accurate results that best meet the query conditions over incomplete database is not a trivial task. Several techniques have been proposed to process queries in incomplete database. Some of these techniques retrieve the query results based on the existing values rather than estimating the missing values. Such techniques are undesirable in many cases as the dimensions with missing values might be the important dimensions of the user’s query. Besides, the output is incomplete and might not satisfy the user preferences. In this paper we propose an approach that estimates missing values in skylines to guide users in selecting the most appropriate skylines from the several candidate skylines. The approach utilizes the concept of mining attribute correlations to generate an Approximate Functional Dependencies (AFDs) that captured the relationships between the dimensions. Besides, identifying the strength of probability correlations to estimate the values. Then, the skylines with estimated values are ranked. By doing so, we ensure that the retrieved skylines are in the order of their estimated precision.
format Article
author Aljuboori, Ali A.Alwan
Ibrahim, Hamidah
Udzir, Nur Izura
Sidi, Fatimah
author_facet Aljuboori, Ali A.Alwan
Ibrahim, Hamidah
Udzir, Nur Izura
Sidi, Fatimah
author_sort Aljuboori, Ali A.Alwan
title Missing values estimation for skylines in incomplete database
title_short Missing values estimation for skylines in incomplete database
title_full Missing values estimation for skylines in incomplete database
title_fullStr Missing values estimation for skylines in incomplete database
title_full_unstemmed Missing values estimation for skylines in incomplete database
title_sort missing values estimation for skylines in incomplete database
publisher Zarqa University, Jordan
publishDate 2018
url http://irep.iium.edu.my/47989/
http://irep.iium.edu.my/47989/
http://irep.iium.edu.my/47989/1/47989_Missing%20values%20estimation%20for%20skylines.pdf
http://irep.iium.edu.my/47989/2/47989_Missing%20values%20estimation%20for%20skylines_SCOPUS.pdf
http://irep.iium.edu.my/47989/3/47989_Missing%20values%20estimation%20for%20skylines_WOS.pdf
first_indexed 2023-09-18T21:08:12Z
last_indexed 2023-09-18T21:08:12Z
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