Semantically equivalent model for quality evaluation

Kara, Madjid; Lamouchi, Olfa; Ramdane-Chérif, Amar

doi:10.1145/3018896.3056776

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…While Simard et al [114] have proposed a broad framework that focuses on the measurement and categorization of data and tries to offer a measure of the amount of uncertainty based on four major characteristics of accuracy, completeness, consistency, and timeliness. On the other hand, Kara et al [108] presented a new approach which propose a global data quality model in order to obtain a general method of manipulating and evaluating different factors of data quality. This model combines many data quality models that are linked by five types of equivalence relations to indicate the relationship between two criteria of different models, this relations may be D-Similar, S-Similar, D-Same, S-Same, or S-different.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

A systematic literature review on data quality assessment

Reda,

Benabdellah,

Zellou

2023

Bulletin EEI

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Defining and evaluating data quality can be a complex task as it varies depending on the specific purpose for which the data is intended. To effectively assess data quality, it is essential to take into account the intended use of the data and the specific requirements of the data users. It is important to recognize that a standardized approach to data quality assessment (DQA) may not be suitable in all cases, as different uses of data may have distinct quality criteria and considerations. In order to advance research in the field of data quality, it is useful to determine the current state of the art by identifying, evaluating, and analyzing relevant research conducted in recent years. In light of this objective, the study proposes a systematic literature review (SLR) as a suitable approach to examine the landscape of data quality and investigate available research specifically pertaining to DQA. The findings of our SLR clearly reveal and demonstrate the criticality of data quality and point to new directions for future study and have consequences for researchers and practitioners interested in defining and assessing data quality.

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Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%