Quality awareness for a Successful Big Data Exploitation

Cappiello, Cinzia; Samá, Walter; Vitali, Monica

doi:10.1145/3216122.3216124

Cited by 16 publications

(18 citation statements)

References 11 publications

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“…The data comes in various types and formats, which makes it difficult to identify, process, and filter the low-quality data. Most of the data generated are in semi-structured and unstructured data [23], which require the availability of the methods that can evaluate different types of data [29].…”

Section: Qualitative Findingsmentioning

confidence: 99%

“…The timelines of the data is very short, hence, organizations need to collect the data in a reasonable time before any changes occur x x x [30] x [31] x [32] x [33] x [34] x [9]. Furthermore, if organizations were unable to collect the required data, it may introduce uncertainties on the dataset, potentially leading to decision errors [23,29]. Even though volume is the most effortless characteristic of Big Data to be defined, it also causes various challenges [36].…”

Section: Qualitative Findingsmentioning

confidence: 99%

See 1 more Smart Citation

A Thematic Review on Data Quality Challenges and Dimension in the Era of Big Data

Ridzuan

Zainon

Zairul

2021

Lecture Notes in Electrical Engineering

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Data quality is the primary concern faced by most of the organizations due to improper maintenance in the database. Data obtained from the various resources are dirty, affecting the accuracy of predicted results. There are a lot of challenges when handling Big Data because it requires well-defined and precise measurement processes. The challenges are in the characteristics of big data itself where the V's play an important role in measuring and determining data quality. Although the issue has been discussed over 20 years, there is no guideline in identifying the important dimension of data quality being proposed to adhere with the context of Big Data. Therefore, the purpose of this systematic review is to review literature on the issue, challenges, and dimension of data quality in the era of Big Data using thematic review. This review included journal and conference proceeding papers from ACM Digital Library, Scopus, and Science Direct published between 2016 until 2020. Inclusion and exclusion processes have filtered out 21 final articles for the review. A systematic review on these 21 articles focuses on the issue, challenges, and dimension of data quality. The results of this study benefit the future study on the development of data quality dimensions and can be a guideline for the researcher to design the data quality assessment framework.

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Section: Qualitative Findingsmentioning

confidence: 99%

Section: Qualitative Findingsmentioning

confidence: 99%

A Thematic Review on Data Quality Challenges and Dimension in the Era of Big Data

Ridzuan

Zainon

Zairul

2021

Lecture Notes in Electrical Engineering

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“…According to the above related works on Big Data quality assessment, many methods [Auer and Felderer 2019;Cappiello et al 2019Cappiello et al , 2018Taleb et al 2016] only assess the quality of data when Big Data Systems: A Software Engineering Perspective 110:33 Table 13. Some Open Research Challenges in Developing BDSs…”

Section: Recoverabilitymentioning

confidence: 99%

Big Data Systems

Davoudian

Liu

2020

ACM Comput. Surv.

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Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

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“…As regards data quality dimensions, the data quality assessment module is able to evaluate the accuracy, completeness, consistency, distinctness, precision and timeliness, as also described in [39]. Accuracy is a measure of correctness: a value is considered correct if it belongs to the domain of values accepted for a specific attribute [41].…”

Section: ) Dqaas Architecturementioning

confidence: 99%