Data quality: A survey of data quality dimensions

Sidi, Fatimah; Panahy, Payam Hassany Shariat; Affendey, Lilly Suriani; Jabar, Marzanah A.; Ibrahim, Hamidah; Mustapha, Aida

doi:10.1109/infrkm.2012.6204995

Cited by 162 publications

(132 citation statements)

References 12 publications

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“…Facilitate factual examination is then being utilized to recognize data quality issue zone. The utilized of PSP/IQ is because of the target of accomplishing great data guided by the measurements qualities specifically, inborn, illustrative, logical and availability [32]. Another case of information quality administration display is Data Quality Management Maturity Model (DQMMM).…”

Section: Methodical Administration Of Information Quality In Associationmentioning

confidence: 99%

A study on analytical framework to breakdown conditions among data quality measurements

Deshai¹,

Varma²,

Ramana³

2017

IJET

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The point of this survey is to feature issues in information quality research and to talk about potential research chance to accomplish high information quality inside an association. The survey received deliberate writing audit technique in view of research articles distributed in diaries and gathering procedures. Here built up an audit technique in light of particular subjects, for example, ebb and flow inquire about territory in information quality, basic measurements in information quality, information quality administration model and approaches and information quality evaluation strategies. In light of the audit methodology, here select pertinent research articles, concentrate and amalgamation the data to answer an examination questions. The survey features the headway of information quality research to take after its genuine application and talk about the accessible hole for future research. Research territory, for example, association's administration, information quality effect towards the association and database related specialized answers for information quality overwhelmed the early years of information quality research. Be that as it may, since the Web is presently occurring as the new data source, the rising of new research regions, for example, information quality evaluation for web and huge information is unavoidable. This audit additionally recognizes and talks about basic information quality measurements in association, for example, information fulfillment, consistency, exactness and convenience. Likewise think about and feature holes in information quality administration model and procedures. This survey is critical to feature and break down restriction of existing information quality research identified with the current needs in information quality, for example, unstructured information sort and enormous information.

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Section: Methodical Administration Of Information Quality In Associationmentioning

confidence: 99%

A study on analytical framework to breakdown conditions among data quality measurements

Deshai¹,

Varma²,

Ramana³

2017

IJET

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“…They can be used for measuring the productivity or determining the effects of new IS solution interventions (learning curve or repetition when used in parallel with existing practices). The F4W solutions are defined in [31; 32] There exist several frameworks that can be used for measuring the quality of these kind of systems such as: KMS [33] for Knowledge Management Systems; [34] for Team Supporting Tools; [35; 36] for Data Management; [37] for Semantic Workflow Engine Metrics; or [38] for HMI measurements based on logged information [26]. There are quality system measurements and productivity measurements that are closely related, in particular, when considering systems supporting the sharing of knowledge between workers.…”

Section: Review Of Technological Approachesmentioning

confidence: 99%

An Evaluation Framework for Worker-Centric Solutions in Production Environments

Hannola¹,

Lacuera-Pérez²,

Steinhueser³

et al. 2018

dtetr

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Evaluating how an Information Systems (IS) intervention in the workplace affects daily work and impacts on workers and organizations is a challenge that requires a very broad research approach. IS researchers have derived several models to explain and measure IS success, taking various perspectives and system types into account. This study presents an evaluation framework for measuring the impacts of an IS intervention especially at the shop floor in production environments. In this framework, we take a broad scope of examination and apply an integrated model that comprises elements from several methods for analyzing the acceptance and the impact of the new solutions. Thus, the aim is to further develop and enhance the existing methods and models for measuring the acceptance and the impacts of the sociotechnical interventions in production environments.

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“…The task of information scoring is mainly addressed through the decomposition of its quality into components, assessed on different dimensions whose list and definitions vary depending on the author. Some examples are relevance and truthfulness [13], reliability and certainty [10], source-trustworthiness and information-credibility [2], sincerity, competence, intention of the source and plausibility [11] or trust [18,4,8,15], see [17] for a complete list. One recent approach, introduced in [15], considers trust evaluation in a multivalued logic framework based on four dimensions: reliability and competence, which evaluate the source, and plausibility and credibility, which relate to the information content, spanning the range from source to information, from general to contextual and from subjective to objective.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Trust Scoring of Railway Sensor Information

Lenart

Bielecki

Lesot

et al. 2018

Artificial Intelligence and Soft Computing

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Abstract. A sensor can encounter many situations where its readings can be untrustworthy and the ability to recognise this is an important and challenging task. It opens the possibility to assess sensors for forensic or maintenance purposes, compare them or fuse their information. We present a proposition to score a piece of information produced by a sensor as an aggregation of three dimensions called reliability, likelihood and credibility into a trust value that take into account a temporal component. The approach is validated on data from the railway domain.

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Data quality: A survey of data quality dimensions

Cited by 162 publications

References 12 publications

A study on analytical framework to breakdown conditions among data quality measurements

A study on analytical framework to breakdown conditions among data quality measurements

An Evaluation Framework for Worker-Centric Solutions in Production Environments

Dynamic Trust Scoring of Railway Sensor Information

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