Data quality certification using ISO/IEC 25012: Industrial experiences

Gualo, Fernando; Rodríguez, Moisés; Verdugo, Javier; Caballero, Ismael; Piattini, Mario

doi:10.1016/j.jss.2021.110938

Cited by 39 publications

(19 citation statements)

References 6 publications

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“…It is not easy to define data quality, due to its many dimensions [46]. Not all dimensions may be relevant to a specific scenario [47], and different organisations would value quality attributes differently [18]. For instance, not all organisations would prioritise data confidentiality.…”

Section: B Identifying Data Quality Attributesmentioning

confidence: 99%

Data Quality for Software Vulnerability Datasets

Croft¹,

Babar²,

Kholoosi³

2023

Preprint

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The use of learning-based techniques to achieve automated software vulnerability detection has been of longstanding interest within the software security domain. These data-driven solutions are enabled by large software vulnerability datasets used for training and benchmarking. However, we observe that the quality of the data powering these solutions is currently ill-considered, hindering the reliability and value of produced outcomes. Whilst awareness of software vulnerability data preparation challenges is growing, there has been little investigation into the potential negative impacts of software vulnerability data quality. For instance, we lack confirmation that vulnerability labels are correct or consistent. Our study seeks to address such shortcomings by inspecting five inherent data quality attributes for four state-of-the-art software vulnerability datasets and the subsequent impacts that issues can have on software vulnerability prediction models. Surprisingly, we found that all the analyzed datasets exhibit some data quality problems.In particular, we found 20-71% of vulnerability labels to be inaccurate in real-world datasets, and 17-99% of data points were duplicated. We observed that these issues could cause significant impacts on downstream models, either preventing effective model training or inflating benchmark performance. We advocate for the need to overcome such challenges. Our findings will enable better consideration and assessment of software vulnerability data quality in the future.

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Section: B Identifying Data Quality Attributesmentioning

confidence: 99%

Data Quality for Software Vulnerability Datasets

Croft¹,

Babar²,

Kholoosi³

2023

Preprint

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“…In the validation process, we refer to good-quality data characteristics (Gualo et al, 2021) to assess the quality of the dataset we use. Using characteristic several steps of data validation are performed.…”

Section: Data Validationmentioning

confidence: 99%

Application of smart visualisation in the analysis of Hungarian Agriculture ICT

Salais

Botos²,

Felföldi³

2023

JAI

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Agriculture has a certain specialty in information technology, and the application of new ICT technologies is inevitable. First, we discuss the idea behind data, concepts such as data storytelling, types and proper use of visualisation. There are several well-known visualisation types, but we would like to point out some AI (Artificial Intelligence) based ones (decomposition tree, treemap, Q&A). Our institute has prepared a survey to investigate ICT usage and attitudes of companies in the agri-food sector. The importance of business processes that can be supported by ICT had to be evaluated by the companies. In this study, we evaluated the importance of some ICT indicators. To ensure the use of the mentioned intelligent visualisation, we present some examples from our institute's questionnaire on ICT use in agriculture. We found the smart visualisation usable and the idea of smart dashboards seems to be usable in practice.

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“…Veri kalitesi çok boyutlu bir kavram olup içerisinde doğruluk, tamlık, tutarlılık, güvenirlilik, geçerlilik gibi birçok parametresi olan bir kavramdır [6]. Veri kalitesinin yüksek olması bilgi sistemlerinin en temel ihtiyacıdır.…”

Section: Veri Kalitesiunclassified

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

KOÇAK

ERGÜN

2023

Disiplinlerarası Yenilik Araştırmaları Dergisi

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Veri günümüzde çok sık karşılaşılan bir terimdir. Verinin doğru kullanımı doğru değerlendirmeyi sağlar. Bu da kaynakların verimli kullanımını, verilen hizmetin kalitesinin artmasını sağlamaktadır. Verinin en çok toplandığı alanların başında sağlık sektörü gelmektedir. Sağlık hizmet sunumunun maddi ve manevi yükü ağırdır. Bu hizmetin en iyi şekilde verilmesi, kaynakların doğru kullanılması ile yakın ilişkilidir. Sağlık verilerinden anlamlı sonuçların çıkarılarak hekimlere, hemşirelere ve sağlık yöneticileri gibi sağlık sektörü çalışanlarına yön gösterecek bilgilerin sağlanması sağlık verilerinin büyüklüğü düşünüldüğünde ancak veri madenciliği metotları ile mümkündür. Sağlık sektörünün insan hayatını direkt etkileyen bir doğası olması sebebi ile sağlıkta kullanılan verilerin kalitesinin en üst düzeyde olması beklenmektedir. Bu çalışmada veri kalitesini ve veri madenciliğini bütüncül olarak ele almıştır. Uygulama örnekleri aracılığıyla veri madenciliği ile sağlık sektöründe ne tür çalışmalar yapılabileceğine dair genel bir bakış açısı sağlanmıştır.

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Data quality certification using ISO/IEC 25012: Industrial experiences

Cited by 39 publications

References 6 publications

Data Quality for Software Vulnerability Datasets

Data Quality for Software Vulnerability Datasets

Application of smart visualisation in the analysis of Hungarian Agriculture ICT

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

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