Enabling Data Legitimacy in Data-Driven Projects

Batista, André F. M.; Silva, Daniel L. da; Corrêa, Pedro Luiz Pizzigatti

doi:10.1109/ctceec.2017.8455158

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…In addition, different metrics were identified to represent the veracity of web data. Some have related data veracity to the confidence level (i.e., integrity, availability, completeness, consistency, and accuracy) of the data and its sources (Berti-Équille, 2015;Herrera et al, 2019;Patgiri and Ahmed, 2016;Moyne and Iskandar, 2017;Batista et al, 2017). Debattista measured veracity in terms of dereferenceability and consistency (Debattista et al, 2015).…”

Section: Definitionmentioning

confidence: 99%

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Assiri

2020

ADCAIJ

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Data is an essential part of smart cities, and data can play an important role indecision making processes. Data generated through web applications and devicesutilize the Internet of Things (IoT) and related technologies. Thus, it is also importantto be able to create big data, which has historically been defined as having threekey dimensions: volume, variety, and velocity. However, recently, veracity has beenadded as the fourth dimension. Data veracity relates to the quality of the data. Anypotential issues with the quality of the data must be corrected because low-quality dataleads to poor software construction, and ultimately bad decision making. In this work,we reviewed the existing literature on related technical solutions that address dataveracity based on the domain of its application, including social media, web, and IoTapplications. The challenges or limitations and related gaps in existing work will bediscussed, and future research directions will be proposed to address the critical issuesof data veracity in the era of big data

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Section: Definitionmentioning

confidence: 99%

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Assiri

2020

ADCAIJ

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“…Data veracity is the ability to understand the data and the analytical process applied to a dataset. It covers aspects related to confidence in the dataset or data source, for example data integrity, availability, completeness, consistency, and accuracy and in addition, transparency and clarity in the processes used to generate, improve and analyse the dataset [2,8,9]. Authors in [10] discuss a general list of causes that frequently affect data veracity:  Measurement system limits: For example, equipment calibration, human errors, and nonstandard measurement processes.…”

Section: Data Veracitymentioning

confidence: 99%

Failure Mode & Effect Analysis and another Methodology for Improving Data Veracity and Validity

Herrera¹,

Walshaw²,

Bailey³

2020

AETiC

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Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications. In this paper we extend its usage for data veracity and validity improvement in the context of big data analysis and discuss its application in an electronics manufacturing test procedure which consists of a sequence of tests. Finally, we describe another methodology, developed as a result of the DVV-FMEA application which is aimed at improving the tests' repeatability and failure detection capabilities as well as monitoring their reliability.

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Section: A Data Veracitymentioning

confidence: 99%

Failure Mode & Effect Analysis for Improving Data Veracity and Validity

Herrera

Walshaw

Bailey

et al. 2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications, including electronics manufacturing. In this paper we propose a modification of this method to extend its application for data veracity and validity improvement. The proposed DVV-FMEA method is based on engineering features and in addition, provides transparency and understandability of the data and its pre-processing, making it reproducible and trustful.

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Enabling Data Legitimacy in Data-Driven Projects

Cited by 3 publications

References 6 publications

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Failure Mode & Effect Analysis and another Methodology for Improving Data Veracity and Validity

Failure Mode & Effect Analysis for Improving Data Veracity and Validity

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