Big Data Quality: A Quality Dimensions Evaluation

Taleb, Ikbal; Kassabi, Hadeel T. El; Serhani, Mohamed Adel; Dssouli, Rachida; Bouhaddioui, Chafik

doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0122

Cited by 56 publications

(35 citation statements)

References 19 publications

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“…In articles [19,33], the authors have discussed the data quality analytics frameworks to assess the quality of data received from the physiological sensors. These frameworks support data quality assessment based on the data quality metrics and rules.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Normally the data quality is assessed with some business rules that can be applied to the data. For example, to measure the accuracy aspect of data quality, the percentage of values with errors is divided by the total number of values in a data instance [19]. However, the contextual data quality assessment is a complex mechanism that needs automatic learning procedures to identify many patterns of incorrect or malicious data.…”

Section: Introductionmentioning

confidence: 99%

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Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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“…Another work done by Taleb et al [9] in which they propose a system for assessing the accuracy, completeness and www.ijacsa.thesai.org consistency of Big Data based primarily on data sampling. The adopted principle is to create a set of samples, without replacement, from the original dataset.…”

Section: Related Workmentioning

confidence: 99%

Towards a Powerful Solution for Data Accuracy Assessment in the Big Data Context

Talha¹,

Elmarzouqi²,

Kalam³

2020

IJACSA

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Data Accuracy is one of the main dimensions of Data Quality; it measures the degree to which data are correct. Knowing the accuracy of an organization's data reflects the level of reliability it can assign to them in decision-making processes. Measuring data accuracy in Big Data environment is a process that involves comparing data to assess with some "reference data" considered by the system to be correct. However, such a process can be complex or even impossible in the absence of appropriate reference data. In this paper, we focus on this problem and propose an approach to obtain the reference data thanks to the emergence of Big Data technologies. Our approach is based on the upstream selection of a set of criteria that we define as "Accuracy Criteria". We use furthermore a set of techniques such as Big Data Sampling, Schema Matching, Record Linkage, and Similarity Measurement. The proposed model and experiment results allow us to be more confident in the importance of data quality assessment solution and the configuration of the accuracy criteria to automate the selection of reference data in a Data Lake.

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“…Nevertheless, data diffusion is not the only priority to have a good knowledge system on the labor market also data 1 https://ec.europa.eu/esco/portal quality has to be assessed. Data quality has always been the focus of researchers' attention for the many challenges it faces [7] [8]. Several methodologies have been developed to enhance as well as to assess data quality [9].…”

Section: Introductionmentioning

confidence: 99%

An Ontology Driven ESCO LOD Quality Enhancement

Kahlawi¹

2020

IJACSA

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The labor market is a system that is complex and difficult to manage. To overcome this challenge, the European Union has launched the ESCO project which is a language that aims to describe this labor market. In order to support the spread of this project, its dataset was presented as linked open data (LOD). Since LOD is usable and reusable, a set of conditions have to be met. First, LOD must be feasible and high quality. In addition, it must provide the user with the right answers, and it has to be built according to a clear and correct structure. This study investigates the LOD of ESCO, focusing on data quality and data structure. The former is evaluated through applying a set of SPARQL queries. This provides solutions to improve its quality via a set of rules built in first order logic. This process was conducted based on a new proposed ESCO ontology.

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Big Data Quality: A Quality Dimensions Evaluation

Cited by 56 publications

References 19 publications

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Towards a Powerful Solution for Data Accuracy Assessment in the Big Data Context

An Ontology Driven ESCO LOD Quality Enhancement

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