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Relevant bibliographies by topics / Data quality ‪(DQ)‬

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Academic literature on the topic 'Data quality ‪(DQ)‬'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Data quality ‪(DQ)‬"

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Aljumaili, Mustafa, Ramin Karim, and Phillip Tretten. "Metadata-based data quality assessment." VINE Journal of Information and Knowledge Management Systems 46, no. 2 (2016): 232–50. http://dx.doi.org/10.1108/vjikms-11-2015-0059.

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Purpose The purpose of this paper is to develop data quality (DQ) assessment model based on content analysis and metadata analysis. Design/methodology/approach A literature review of DQ assessment models has been conducted. A study of DQ key performances (KPIs) has been done. Finally, the proposed model has been developed and applied in a case study. Findings The results of this study shows that the metadata data have important information about DQ in a database and can be used to assess DQ to provide decision support for decision makers. Originality/value There is a lot of DQ assessment in th

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Syed, Rehan, Rebekah Eden, Tendai Makasi, et al. "Digital Health Data Quality Issues: Systematic Review." Journal of Medical Internet Research 25 (March 31, 2023): e42615. http://dx.doi.org/10.2196/42615.

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Background The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective The aim of this study was to develop a

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Batini, C., T. Blaschke, S. Lang, et al. "DATA QUALITY IN REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 447–53. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-447-2017.

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The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO

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Anantharama, Nandini, Wray Buntine, and Andrew Nunn. "A Systematic Approach to Reconciling Data Quality Failures: Investigation Using Spinal Cord Injury Data." ACI Open 05, no. 02 (2021): e94-e103. http://dx.doi.org/10.1055/s-0041-1735975.

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Abstract Background Secondary use of electronic health record's (EHR) data requires evaluation of data quality (DQ) for fitness of use. While multiple frameworks exist for quantifying DQ, there are no guidelines for the evaluation of DQ failures identified through such frameworks. Objectives This study proposes a systematic approach to evaluate DQ failures through the understanding of data provenance to support exploratory modeling in machine learning. Methods Our study is based on the EHR of spinal cord injury inpatients in a state spinal care center in Australia, admitted between 2011 and 20

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Nazaire, Mare. "Integrating Data Quality Feedback: a Data Provider's Perspective." Biodiversity Information Science and Standards 2 (June 13, 2018): e26007. https://doi.org/10.3897/biss.2.26007.

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The Herbarium of Rancho Santa Ana Botanic Garden [RSA-POM] is the third largest herbarium in California and consists of >1.2 million specimens, of which ~50% are digitized. As a data provider, RSA-POM publishes its data with several aggregators, including the Consortium of California Herbaria, JSTOR, Symbiota (which is subsequently pulled into iDigBio and GBIF), as well as its own local webportal. Each submission of data needs to be prepared and formatted according to the aggregator's specifications for publication. Feedback on data quality (DQ) ranges from an individual user (often only a

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Paul, Deborah, and Nicole Fisher. "Challenges For Implementing Collections Data Quality Feedback: synthesizing the community experience." Biodiversity Information Science and Standards 2 (June 13, 2018): e26003. https://doi.org/10.3897/biss.2.26003.

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Much data quality (DQ) feedback is now available to data providers from aggregators of collections specimen and related data. Similarly, transcription centres and crowdsourcing platforms also provide data that must be assessed and often manipulated before uploading to a local database and subsequently published with aggregators. In order to facilitate broader DQ information use aggregators (GBIF, ALA, iDigBio, VertNet) and others, through the TDWG BDQ Interest Group, are harmonizing the DQ information provided - transforming part of the DQ feedback process. But, collections sharing data face c

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Veiga, Allan, and Antonio Saraiva. "Defining a Data Quality (DQ) profile and DQ report using a prototype of Node.js module of the Fitness for Use Backbone (FFUB)." Biodiversity Information Science and Standards 1 (August 14, 2017): e20275. https://doi.org/10.3897/tdwgproceedings.1.20275.

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Despite the increasing availability of biodiversity data, determing the quality of data and informing would-be data consumers and users remains a significant issue. In order for data users and data owners to perform a satisfactory assessment and management of data fitness for use, they require a Data Quality (DQ) report, which presents a set of relevant DQ measures, validations, and amendments assigned to data. Determining the meaning of "fitness for use" is essential to best manage and assess DQ. To tackle the problem, the TDWG Biodiversity Data Quality (BDQ) - Interest Group (IG) (https://gi

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Bian, Jiang, Tianchen Lyu, Alexander Loiacono, et al. "Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data." Journal of the American Medical Informatics Association 27, no. 12 (2020): 1999–2010. http://dx.doi.org/10.1093/jamia/ocaa245.

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Abstract Objective To synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet). Materials and Methods We started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment

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Blake, Roger, and Ganesan Shankaranarayanan. "Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis." International Journal of Business Intelligence Research 3, no. 1 (2012): 1–16. http://dx.doi.org/10.4018/jbir.2012010101.

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In the recent decade, the field of data and information quality (DQ) has grown into a research area that spans multiple disciplines. The motivation here is to help understand the core topics and themes that constitute this area and to determine how those topics and themes from DQ relate to business intelligence (BI). To do so, the authors present the results of a study which mines the abstracts of articles in DQ published over the last decade. Using Latent Semantic Analysis (LSA) six core themes of DQ research are identified, as well as twelve dominant topics comprising them. Five of these top

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Veiga, Allan, and Antonio Saraiva. "Toward a Biodiversity Data Fitness for Use Backbone (FFUB): A Node.js module prototype." Biodiversity Information Science and Standards 1 (August 14, 2017): e20300. https://doi.org/10.3897/tdwgproceedings.1.20300.

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Introduction: The Biodiversity informatics community has made important achievements regarding digitizing, integrating and publishing standardized data about global biodiversity. However, the assessment of the quality of such data and the determination of the fitness for use of those data in different contexts remain a challenge. To tackle such problem using a common approach and conceptual base, the TDWG Biodiversity Data Quality Interest Group - BDQ-IG (https://github.com/tdwg/bdq) has proposed a conceptual framework to define the necessary components to describe Data Quality (DQ) needs, DQ

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Books on the topic "Data quality ‪(DQ)‬"

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Morbey, Guilherme. Data Quality for Decision Makers: A Dialog Between a Board Member and a DQ Expert. Springer Gabler. in Springer Fachmedien Wiesbaden GmbH, 2013.

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Morbey, Guilherme. Data Quality for Decision Makers: A dialog between a board member and a DQ expert. Springer Gabler, 2013.

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Book chapters on the topic "Data quality ‪(DQ)‬"

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Rivas, Bibiano, Jorge Merino, Manuel Serrano, Ismael Caballero, and Mario Piattini. "I8K|DQ-BigData: I8K Architecture Extension for Data Quality in Big Data." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25747-1_17.

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Sáez Carlos, Martínez-Miranda Juan, Robles Montserrat, and García-Gómez Juan Miguel. "Organizing Data Quality Assessment of Shifting Biomedical Data." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-101-4-721.

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Low biomedical Data Quality (DQ) leads into poor decisions which may affect the care process or the result of evidence-based studies. Most of the current approaches for DQ leave unattended the shifting behaviour of data underlying concepts and its relation to DQ. There is also no agreement on a common set of DQ dimensions and how they interact and relate to these shifts. In this paper we propose an organization of biomedical DQ assessment based on these concepts, identifying characteristics and requirements which will facilitate future research. As a result, we define the Data Quality Vector c

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Weber Jens H., Price Morgan, and Davies Iryna. "Taming the Data Quality Dragon – A Theory and Method for Data Quality by Design." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-928.

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A lack of data quality (DQ) is often a significant inhibitor impeding the realization of cost and quality benefits expected from Clinical Information Systems (CIS). Attaining and sustaining DQ in CIS has been a multi-faceted and elusive goal. The current literature on DQ in health informatics mainly consists of empirical studies and practitioners' reports, but often lack a holistic approach to addressing DQ &lsquo;by design&rsquo;. This paper seeks to present a general framework for clinical DQ, which blends foundational engineering theories with concepts and methods from health inform

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Sundararaman, Arun Thotapalli. "Data Quality for Data Mining in Business Intelligence Applications." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6477-7.ch003.

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Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of th

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Rahimi, Alireza, Siaw-Teng Liaw, Pradeep Kumar Ray, Jane Taggart, and Hairong Yu. "Ontology for Data Quality and Chronic Disease Management." In Healthcare Informatics and Analytics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6316-9.ch016.

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Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontology-based approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with

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Tute Erik. "Striving for Use Case Specific Optimization of Data Quality Assessment for Health Data." In Studies in Health Technology and Informatics. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-880-8-113.

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Data quality (DQ) assessment is advisable before (re)using datasets. Besides supporting DQ-assessment, DQ-tools can indicate data integration issues. The objective of this contribution is to put up for discussion the identified current state of scientific knowledge in DQ-assessment for health data and the planned work resulting from that state of knowledge. The state of scientific knowledge bases on a continuous literature survey and tracking of related working groups' activities. 95 full text publications constitute the considered state of scientific knowledge of which a representative select

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Tahar, Kais, Raphael Verbuecheln, Tamara Martin, Holm Graessner, and Dagmar Krefting. "Local Data Quality Assessments on EHR-Based Real-World Data for Rare Diseases." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230121.

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The project “Collaboration on Rare Diseases” CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL proce

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Triefenbach, Lucas, Ronny Otto, Jonas Bienzeisler, et al. "Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry – A Secondary Use Perspective." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220439.

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Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a D

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Sundararaman, Arun Thotapalli. "Big Data Quality for Data Mining in Business Intelligence Applications." In Advances in Business Information Systems and Analytics. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5781-5.ch004.

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Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of

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Sundararaman, Arun Thotapalli. "Effective Measurement of DQ/IQ for BI." In Information Quality and Governance for Business Intelligence. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4892-0.ch012.

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DQ/IQ measurement in general and in the specific context of BI has always been a topic of high interest for researchers. The topic of Data Quality (DQ) in the field of Information Management has been well researched, published, and studied. Despite such research advances, there has been very little understanding either from a theoretical or from a practical perspective of DQ/IQ measurement for BI. Assessing the quality of data for a BI System has been one of the major challenges for researchers as well as practitioners, leading to the need for frameworks to measure DQ for BI. The objective of

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Conference papers on the topic "Data quality ‪(DQ)‬"

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Veiga, Allan Koch, and Antonio Mauro Saraiva. "Biodiversity Data Quality Profiling: A practical guideline." In VIII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação - SBC, 2017. http://dx.doi.org/10.5753/wcama.2017.3441.

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A crescente disponibilidade de dados de biodiversidade em todo o mundo, providos por um número crescente de instituições, e o crescente uso desses dados para uma variedade de usos suscitaram preocupações relacionadas a "adequação ao uso" desses dados e o impacto nos resultados desses usos. Para abordar estas questões, definiu-se um framework conceitual no contexto do Biodiversity Information Standards (TDWG) para servir como uma abordagem consistente para avaliar e gerir a Qualidade dos Dados (QD) em dados da biodiversidade. Com base neste quadro, propomos um método para definir Perfis DQ que

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Ehrlinger, Lisa, Alexander Gindlhumer, Lisa-Marie Huber, and Wolfram Wöß. "DQ-MeeRKat: Automating Data Quality Monitoring with a Reference-Data-Profile-Annotated Knowledge Graph." In 10th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010546202150222.

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Ehrlinger, Lisa, Alexander Gindlhumer, Lisa-Marie Huber, and Wolfram Wöß. "DQ-MeeRKat: Automating Data Quality Monitoring with a Reference-Data-Profile-Annotated Knowledge Graph." In 10th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010546200002993.

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Parody, Luisa, Maria Teresa Gomez-Lopez, Isabel Bermejo, Ismael Caballero, Rafael M. Gasca, and Mario Piattini. "PAIS-DQ: Extending process-aware information systems to support data quality in PAIS life-cycle." In 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS). IEEE, 2016. http://dx.doi.org/10.1109/rcis.2016.7549342.

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Pei, Shuiqiang, Xiaoguang Hu, Guofeng Zhang, and Li Fu. "Improved Voltage Sag Detection Method and Optimal Design for the Digital Low-Pass Filter in Small UAVs." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46401.

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Real-time and accurate detection of the voltage sag characteristics is the premise to achieve dynamic voltage restorer compensation. An improved αβ-dq transformation detection method is presented for the limitations of traditional detection methods. In this method, the α-axis component of the αβ static coordinate system is deduced according to the single-phase voltage. The virtual β-axis component is constructed from the derivative of the α-axis component. The magnitudes, duration, phase-angle jump of the voltage sag are detected quickly and accurately by αβ-dq transformation and low-pass filt

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Zeng, Y., S. Ryu, C. G. Chaney, et al. "Finding the Right Concept Via a Decision Quality Framework with Rapid Generation of Multiple Deepwater Conceptual Alternatives." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210288-ms.

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Abstract The key to finding the highest-value concept in deepwater full-field development is by making high-quality decisions during the Concept Select stage of a project. One of the critical elements to achieve this is by considering a broad range of conceptual alternatives and evaluating them rapidly, providing timely feedback, and facilitating an exploratory learning process. However, concept-select decisions are challenged by competing objectives, significant uncertainties, and many possible concepts. Further, deepwater full-field developments require strong connectivity and interfaces acr

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Patel, Harsh, and Jonathan Chong. "How to Design a Modular, Effective, and Interpretable Machine Learning-Based Real-Time System: Lessons from Automated Electrical Submersible Pump Surveillance." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216761-ms.

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Abstract Many machine learning (ML) projects do not progress beyond the proof-of-concept phase into real-world operations and remain economical at scale. Commonly discussed challenges revolve around digitalization, data, and infrastructure/tooling. However, there are other non-ML aspects that are equally if not more important towards building a successful system. This paper presents a general framework and lessons learned for building a robust, practical, and modular domain-centric ML-based system in contrast to purely "data-centric" or "model-centric" approaches. This paper presents the case

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