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Journal articles on the topic 'Data quality'

To see the other types of publications on this topic, follow the link: Data quality.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 July 2024

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1

Vaníček, J. "Software and data quality." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (February 17, 2012): 138–46. http://dx.doi.org/10.17221/5007-agricecon.

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The paper presents new ideas in the International SQuaRE (Software Quality Requirements and Evaluation) standardisation research project, which concerns the development of a special branch of international standards for software quality. Data can be considered as an integral part of software. The current international standard and technical report of the ISO/IEC 9126, ISO/IEC 14598 series and ISO/IEC 12119 standard covert the whole software as an indivisible entity. However, such data sets as databases and data stores have a special character and need a different structure of quality characteristic. Therefore it was decided in the SQuaRE project create a special international standard for data quality. The main idea for this standard and the critical discussion of these ideas is presented in this paper. The main part of this contribution was presented on the conference Agricultural Perspectives XIV, aligned by Czech University of Agriculture in Prague, September 20 to 21, 2005.

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2

Stoykov, Evgeni. "DATA QUALITY FOR HYDROGRAPHIC MEASUREMENTS." Journal Scientific and Applied Research 21, no. 1 (November 15, 2021): 26–30. http://dx.doi.org/10.46687/jsar.v21i1.314.

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The topic of preservation of cultural heritage is very important and is an integral part of National Security. It is up-to-date and timely. Its significance is determined by the scale and intensity of criminal attacks on cultural heritage, which have caused an increase in the need to update the system of measures to safeguard cultural values and overcome the underestimation of the protection of cultural heritage as a national security factor.

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3

Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (January 2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.

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4

Chicco, Joanne. "Data Quality." Health Information Management 27, no. 1 (March 1997): 12–21. http://dx.doi.org/10.1177/183335839702700106.

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5

Sadiq, Shazia, Tamraparni Dasu, Xin Luna Dong, Juliana Freire, Ihab F. Ilyas, Sebastian Link, Miller J. Miller, Felix Naumann, Xiaofang Zhou, and Divesh Srivastava. "Data Quality." ACM SIGMOD Record 46, no. 4 (February 22, 2018): 35–43. http://dx.doi.org/10.1145/3186549.3186559.

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6

Glaze, William H. "Data quality." Environmental Science & Technology 36, no. 11 (June 2002): 225A. http://dx.doi.org/10.1021/es0223170.

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7

Fan, Wenfei. "Data Quality." ACM SIGMOD Record 44, no. 3 (December 3, 2015): 7–18. http://dx.doi.org/10.1145/2854006.2854008.

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8

Huh, YU, FR Keller, TC Redman, and AR Watkins. "Data quality." Information and Software Technology 32, no. 8 (October 1990): 559–65. http://dx.doi.org/10.1016/0950-5849(90)90146-i.

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9

Kennedy, Dale J., Douglas C. Montgomery, Dwayne A. Rollier, and J. Bert Keats. "Data Quality." International Journal of Life Cycle Assessment 2, no. 4 (December 1997): 229–39. http://dx.doi.org/10.1007/bf02978420.

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10

Kennedy, Dale J., Douglas C. Montgomery, and Beth H. Quay. "Data quality." International Journal of Life Cycle Assessment 1, no. 4 (December 1996): 199–207. http://dx.doi.org/10.1007/bf02978693.

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11

Ozmen-Ertekin, Dilruba, and Kaan Ozbay. "Dynamic data maintenance for quality data, quality research." International Journal of Information Management 32, no. 3 (June 2012): 282–93. http://dx.doi.org/10.1016/j.ijinfomgt.2011.11.003.

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12

Lee, Kyoung Ho, and Jang Ryong Lee. "Factors Affecting LOSA Data Quality." Journal of the Korean Society for Aviation and Aeronautics 31, no. 2 (June 2023): 72–80. http://dx.doi.org/10.12985/ksaa.2023.31.2.072.

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13

Rhazal, Oumaima El. "Improvement of Data Quality in Smart City: Toward Smart Data Aspect." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1198–204. http://dx.doi.org/10.5373/jardcs/v12sp4/20201594.

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14

Golz, Claudia, Thomas Einfalt, and Gianmario Galli. "Radar data quality control methods in VOLTAIRE." Meteorologische Zeitschrift 15, no. 5 (November 17, 2006): 497–504. http://dx.doi.org/10.1127/0941-2948/2006/0151.

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15

Mathes, Armin, Petra Friederichs, and Andreas Hense. "Towards a quality control of precipitation data." Meteorologische Zeitschrift 17, no. 6 (December 9, 2008): 733–49. http://dx.doi.org/10.1127/0941-2948/2008/0347.

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16

Eken, Süleyman, Ahmet Sayar, and Kürşat Topçuoğlu. "AutoTest: Automation to Test Tabular Data Quality." International Journal of Computer and Electrical Engineering 6, no. 4 (2014): 365–68. http://dx.doi.org/10.7763/ijcee.2014.v6.854.

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17

Epstein, Richard H. "Quality Improvement Demands Quality Data." Anesthesia & Analgesia 121, no. 6 (December 2015): 1425–27. http://dx.doi.org/10.1213/ane.0000000000001012.

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18

Gong, Xiaowen, and Ness B. Shroff. "Truthful Data Quality Elicitation for Quality-Aware Data Crowdsourcing." IEEE Transactions on Control of Network Systems 7, no. 1 (March 2020): 326–37. http://dx.doi.org/10.1109/tcns.2019.2905090.

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19

Taggart, Jane, Siaw-Teng Liaw, and Hairong Yu. "Structured data quality reports to improve EHR data quality." International Journal of Medical Informatics 84, no. 12 (December 2015): 1094–98. http://dx.doi.org/10.1016/j.ijmedinf.2015.09.008.

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20

Gill, Rupali, and Jaiteg Singh. "A Review of Contemporary Data Quality Issues in Data Warehouse ETL Environment." Journal on Today's Ideas - Tomorrow's Technologies 2, no. 2 (December 5, 2014): 153–60. http://dx.doi.org/10.15415/jotitt.2014.22012.

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21

De Smet, Bea, and Mark Stalmans. "LCI data and data quality." International Journal of Life Cycle Assessment 1, no. 2 (June 1996): 96–104. http://dx.doi.org/10.1007/bf02978653.

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22

Fletcher, Sam, and Md Zahidul Islam. "Measuring Information Quality for Privacy Preserving Data Mining." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 21–28. http://dx.doi.org/10.7763/ijcte.2015.v7.924.

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23

Kulikowski, Juliusz L. "Data Quality Assessment." International Journal of Organizational and Collective Intelligence 4, no. 1 (January 2014): 24–36. http://dx.doi.org/10.4018/ijoci.2014010102.

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A state-of-the-art in the domain of data quality assessment and maintaining in modern information systems is presented in the paper. A short historical view on the development of this problem is given. Particular attention is paid to the development of the idea of multi-aspect data quality assessment. The problem of extension of single data quality assessment on higher-level data structures is considered. The methods of multi-aspect data quality measures ordering for comparison is analyzed and its solution based on the concept of semi-ordering in linear vector (Kantorovitsh) space is proposed. Remarks on organizational and technological tools for data quality maintenance in organizations are given. Expected future trends in the development of data quality assessment and maintenance methods are suggested.

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24

Neil, Stuart. "Agricultural data quality." Significance 1, no. 1 (March 2004): 30–32. http://dx.doi.org/10.1111/j.1740-9713.2004.00014.x.

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25

Terris, Darcey D., and David G. Litaker. "Data Quality Bias." Quality Management in Health Care 17, no. 1 (January 2008): 19–26. http://dx.doi.org/10.1097/01.qmh.0000308634.59108.60.

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26

Zaveri, Amrapali, Andrea Maurino, and Laure-Berti Equille. "Web Data Quality." International Journal on Semantic Web and Information Systems 10, no. 2 (April 2014): 1–6. http://dx.doi.org/10.4018/ijswis.2014040101.

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The standardization and adoption of Semantic Web technologies has resulted in an unprecedented volume of data being published as Linked Data (LD). However, the “publish first, refine later” philosophy leads to various quality problems arising in the underlying data such as incompleteness, inconsistency and semantic ambiguities. In this article, we describe the current state of Data Quality in the Web of Data along with details of the three papers accepted for the International Journal on Semantic Web and Information Systems' (IJSWIS) Special Issue on Web Data Quality. Additionally, we identify new challenges that are specific to the Web of Data and provide insights into the current progress and future directions for each of those challenges.

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27

Spencer, Bruce D. "Optimal Data Quality." Journal of the American Statistical Association 80, no. 391 (September 1985): 564–73. http://dx.doi.org/10.1080/01621459.1985.10478155.

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28

Bartoli, Alberto, Andrea De Lorenzo, Eric Medvet, and Fabiano Tarlao. "Data Quality Challenge." Journal of Data and Information Quality 6, no. 4 (October 26, 2015): 1–4. http://dx.doi.org/10.1145/2786983.

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29

Pipino, Leo L., Yang W. Lee, and Richard Y. Wang. "Data quality assessment." Communications of the ACM 45, no. 4 (April 2002): 211–18. http://dx.doi.org/10.1145/505248.506010.

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30

Odeh, Inakwu O. A. "Spatial Data Quality." Geoderma 116, no. 3-4 (October 2003): 395–98. http://dx.doi.org/10.1016/s0016-7061(03)00115-0.

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31

CLOUGH, J. D. "Health quality data." Cleveland Clinic Journal of Medicine 63, no. 5 (September 1, 1996): 303–4. http://dx.doi.org/10.3949/ccjm.63.5.303.

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32

Savage, Barbara M. "Managing quality data." Total Quality Management 7, no. 6 (December 1996): 667–74. http://dx.doi.org/10.1080/09544129610540.

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33

BUTTENFIELD, BARBARA P. "Representing Data Quality." Cartographica: The International Journal for Geographic Information and Geovisualization 30, no. 2-3 (October 1993): 1–7. http://dx.doi.org/10.3138/232h-6766-3723-5114.

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34

Tayi, Giri Kumar, and Donald P. Ballou. "Examining data quality." Communications of the ACM 41, no. 2 (February 1998): 54–57. http://dx.doi.org/10.1145/269012.269021.

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35

Moore, Roger A., and DeLaine Schmitz. "Regarding “Quality Improvement Demands Quality Data”." Anesthesia & Analgesia 122, no. 6 (June 2016): 2065. http://dx.doi.org/10.1213/ane.0000000000001230.

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36

Fallowfield, Lesley. "Quality of quality-of-life data." Lancet 348, no. 9025 (August 1996): 421–22. http://dx.doi.org/10.1016/s0140-6736(05)64534-5.

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37

Dravis, F. "Six steps to quality [data quality]." Information Professional 2, no. 5 (October 1, 2005): 16–21. http://dx.doi.org/10.1049/inp:20050502.

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38

Kim, Yong-Hwan, Jin-Hee Lee, Young-Jin Yoon, Sang-Il Lee, and Sung-Jin Kim. "Data Quality Management using Quality-Level Characterization of Public Data." Journal of the Korea Institute of Information and Communication Engineering 27, no. 12 (December 31, 2023): 1465–72. http://dx.doi.org/10.6109/jkiice.2023.27.12.1465.

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39

Pantos, C., M, Schroeder, and P. Ivanova. "Open Access to Quality Biomedical Experimental and Clinical Data and Data-based Models." Biomedical Data Journal 01, no. 1 (January 2015): 1–2. http://dx.doi.org/10.11610/bmdj.01100.

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40

Ali, Basel J. A. "Information Quality and Data Quality in Accounting Information System: Implications on the Organization Performance." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 3258–69. http://dx.doi.org/10.37200/ijpr/v24i5/pr202034.

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41

Bernasconi, Anna. "Data quality-aware genomic data integration." Computer Methods and Programs in Biomedicine Update 1 (2021): 100009. http://dx.doi.org/10.1016/j.cmpbup.2021.100009.

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42

Wilantika, Nori, and Wahyu Catur Wibowo. "Data Quality Management in Educational Data." Jurnal Sistem Informasi 15, no. 2 (October 31, 2019): 52–67. http://dx.doi.org/10.21609/jsi.v15i2.848.

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Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of maturity. Only the data quality dimensions component has achieved the expected target. Thus, recommendations have been proposed based on the DAMA-DMBOK framework. The activities needed to be carried out are developing and promoting awareness of data quality; defining data quality requirements; profiling, analyzing, and evaluating data quality; define business rules for data quality, establish, and evaluate the data quality services levels, manage problems related to data quality, design and implement operational procedures for data quality management, and monitor operations and performance of data quality management procedures.

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43

M. Marvin Newhouse for the MPS Coor. "Data entry design and data quality." Controlled Clinical Trials 6, no. 3 (September 1985): 229. http://dx.doi.org/10.1016/0197-2456(85)90036-4.

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44

Li, Xingsen, Yong Shi, Jun Li, and Peng Zhang. "Data Mining Consulting Improve Data Quality." Data Science Journal 6 (2007): S658—S666. http://dx.doi.org/10.2481/dsj.6.s658.

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45

Ringler, A. T., M. T. Hagerty, J. Holland, A. Gonzales, L. S. Gee, J. D. Edwards, D. Wilson, and A. M. Baker. "The data quality analyzer: A quality control program for seismic data." Computers & Geosciences 76 (March 2015): 96–111. http://dx.doi.org/10.1016/j.cageo.2014.12.006.

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46

Larburu, N., R. G. A. Bults, M. van Sinderen, I. Widya, and H. J. Hermens. "Quality-of-Data Broker for Quality-of-Data-Aware Telemedicine Systems." IRBM 37, no. 4 (August 2016): 210–18. http://dx.doi.org/10.1016/j.irbm.2016.05.002.

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47

Cai, Li, and Yangyong Zhu. "The Challenges of Data Quality and Data Quality Assessment in the Big Data Era." Data Science Journal 14 (May 22, 2015): 2. http://dx.doi.org/10.5334/dsj-2015-002.

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48

Srikanth I Chandan, O. "Advancing Clinical Data Capture: Embracing Electronic Data Capture (EDC) for Enhanced Efficiency and Quality." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 1261–64. http://dx.doi.org/10.21275/sr23717190711.

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49

Côté, Roger A. "Quality health care requires quality patient data." Yearbook of Medical Informatics 12, no. 01 (August 2003): 1–4. http://dx.doi.org/10.1055/s-0038-1638171.

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50

ParkJooSeok, Hocheol Ryu, Jangho Lee, 이준용, 김승현, and 이준기. "Applying Service Quality to Big Data Quality." Korea Journal of BigData 2, no. 2 (December 2017): 87–93. http://dx.doi.org/10.36498/kbigdt.2017.2.2.87.

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