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Статті в журналах з теми "Data quality indicators"
Matute, Jorge, and A. P. Gupta. "Data Quality and Indicators." American Journal of Agricultural and Biological Sciences 2, no. 1 (January 1, 2007): 23–30. http://dx.doi.org/10.3844/ajabssp.2007.23.30.
Повний текст джерелаJohnson, Barry L., T. Damstra, Chris Derosa, C. Elmer, and M. Gilbert. "Workshop on Toxicological Data Quality Indicators." Toxicology and Industrial Health 9, no. 4 (July 1993): 577–604. http://dx.doi.org/10.1177/074823379300900402.
Повний текст джерелаIseni, Jeton, and Olaf Jacob. "I33 Data quality indicators for huntington’s disease observational studies; data quality indicators framework – an explorative study." Journal of Neurology, Neurosurgery & Psychiatry 87, Suppl 1 (September 2016): A70.2—A70. http://dx.doi.org/10.1136/jnnp-2016-314597.198.
Повний текст джерелаWeiss, Manfred S. "Global indicators of X-ray data quality." Journal of Applied Crystallography 34, no. 2 (April 1, 2001): 130–35. http://dx.doi.org/10.1107/s0021889800018227.
Повний текст джерелаCadarette, S. M., S. B. Jaglal, L. Raman-Wilms, D. E. Beaton, and J. M. Paterson. "Osteoporosis quality indicators using healthcare utilization data." Osteoporosis International 22, no. 5 (June 25, 2010): 1335–42. http://dx.doi.org/10.1007/s00198-010-1329-8.
Повний текст джерелаWeiss, M. S., and R. Hilgenfeld. "Global Indicators of X-ray Data Quality." Acta Crystallographica Section A Foundations of Crystallography 56, s1 (August 25, 2000): s105. http://dx.doi.org/10.1107/s0108767300022789.
Повний текст джерелаPulido Moncada, Mansonia, Donald Gabriels, and Wim M. Cornelis. "Data-driven analysis of soil quality indicators using limited data." Geoderma 235-236 (December 2014): 271–78. http://dx.doi.org/10.1016/j.geoderma.2014.07.014.
Повний текст джерелаPickering, Ashley E., Petrus Malherbe, Joan Nambuba, Corey B. Bills, Emilie Calvello Hynes, and Brian Rice. "Clinical emergency care quality indicators in Africa: a scoping review and data summary." BMJ Open 13, no. 5 (May 2023): e069494. http://dx.doi.org/10.1136/bmjopen-2022-069494.
Повний текст джерелаKolozsvári, László Róbert, and Imre Rurik. "Quality improvement in primary care. Financial incentives related to quality indicators in Europe." Orvosi Hetilap 154, no. 28 (July 2013): 1096–101. http://dx.doi.org/10.1556/oh.2013.29631.
Повний текст джерелаSchnelle, John F., Mary P. Cadogan, June Yoshii, Nahla R. Al-Samarrai, Dan Osterweil, Barbara M. Bates-Jensen, and Sandra F. Simmons. "The Minimum Data Set Urinary Incontinence Quality Indicators." Medical Care 41, no. 8 (August 2003): 909–22. http://dx.doi.org/10.1097/00005650-200308000-00005.
Повний текст джерелаДисертації з теми "Data quality indicators"
Tiano, Donato. "Learning models on healthcare data with quality indicators." Electronic Thesis or Diss., Lyon 1, 2022. http://www.theses.fr/2022LYO10182.
Повний текст джерелаTime series are collections of data obtained through measurements over time. The purpose of this data is to provide food for thought for event extraction and to represent them in an understandable pattern for later use. The whole process of discovering and extracting patterns from the dataset is carried out with several extraction techniques, including machine learning, statistics, and clustering. This domain is then divided by the number of sources adopted to monitor a phenomenon. Univariate time series when the data source is single and multivariate time series when the data source is multiple. The time series is not a simple structure. Each observation in the series has a strong relationship with the other observations. This interrelationship is the main characteristic of time series, and any time series extraction operation has to deal with it. The solution adopted to manage the interrelationship is related to the extraction operations. The main problem with these techniques is that they do not adopt any pre-processing operation on the time series. Raw time series have many undesirable effects, such as noisy points or the huge memory space required for long series. We propose new data mining techniques based on the adoption of the most representative features of time series to obtain new models from the data. The adoption of features has a profound impact on the scalability of systems. Indeed, the extraction of a feature from the time series allows for the reduction of an entire series to a single value. Therefore, it allows for improving the management of time series, reducing the complexity of solutions in terms of time and space. FeatTS proposes a clustering method for univariate time series that extracts the most representative features of the series. FeatTS aims to adopt the features by converting them into graph networks to extract interrelationships between signals. A co-occurrence matrix merges all detected communities. The intuition is that if two time series are similar, they often belong to the same community, and the co-occurrence matrix reveals this. In Time2Feat, we create a new multivariate time series clustering. Time2Feat offers two different extractions to improve the quality of the features. The first type of extraction is called Intra-Signal Features Extraction and allows to obtain of features from each signal of the multivariate time series. Inter-Signal Features Extraction is used to obtain features by considering pairs of signals belonging to the same multivariate time series. Both methods provide interpretable features, which makes further analysis possible. The whole time series clustering process is lighter, which reduces the time needed to obtain the final cluster. Both solutions represent the state of the art in their field. In AnomalyFeat, we propose an algorithm to reveal anomalies from univariate time series. The characteristic of this algorithm is the ability to work among online time series, i.e. each value of the series is obtained in streaming. In the continuity of previous solutions, we adopt the functionality of revealing anomalies in the series. With AnomalyFeat, we unify the two most popular algorithms for anomaly detection: clustering and recurrent neural network. We seek to discover the density area of the new point obtained with clustering
Lai, Yuk-lin. "Analysis of incomplete survey data with application to the construction of social indicators of Hong Kong /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19929523.
Повний текст джерелаNtshuntshe-Matshaya, Pateka Patricia. "Investigating the relevance of quality measurement indicators for South African higher education libraries." University of the Western Cape, 2021. http://hdl.handle.net/11394/8337.
Повний текст джерелаThis study investigates the relevance of quality measurement indicators at higher education libraries for faculty academics, librarians, and students. The study followed a mixed-method design with a mixture of quantitative and qualitative data collection. Faculty academics, librarians and students ranked the existing quality measurement indicators for South African higher education libraries. The findings revealed that for library quality measures to meet the needs of faculty academics, librarians, and students, the resources must be accessible both physically and virtually, and staff should be accountable and willing to offer services responsive to the users' needs and expectations of a safe, secure, and comfortable library space, be it physical or virtual. The qualitative data highlighted the importance of adequate resources and the adoption of new developments as measures for quality. Quality measurement indicators must include elements such as adequate funding; relevant resources aligned with teaching and learning programmes; programmes that are integrated into teaching plans; effective supplier collaboration with respect to the process of acquiring relevant learning materials; effective student training; communication of the value of library services and alignment with the student learning outcomes; research support in a digital environment with e-tools and website navigability; research data management; and open access, which is a prominent role of the library. Based on the data, there was a quality measure (process) that was commendable even though it did not form part of the existing quality measures nor a service whose relevance was assessed. The separation of undergraduate and postgraduate learning spaces was amongst those services that ranked quite high from the students' responses (qualitative data). Even though there were differences emphasized on each indicator by either faculty academics or students, there were also discrepancies in the interpretation of what each quality indicator means to each study population group. As the study of this nature has recommendations and gaps identified in terms of research findings, it is quite important to record that there was a series of gaps that were identified in terms of library expectations and perceptions. These gaps were suggested as part of further research that must be conducted to fill the void in terms of library users’ voices in the development of higher education library measurement indicators.
Rojas-Candio, Piero, Arturo Villantoy-Pasapera, Jimmy Armas-Aguirre, and Santiago Aguirre-Mayorga. "Evaluation Method of Variables and Indicators for Surgery Block Process Using Process Mining and Data Visualization." Repositorio Academico - UPC, 2021. http://hdl.handle.net/10757/653799.
Повний текст джерелаIn this paper, we proposed a method that allows us to formulate and evaluate process mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through data visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The proposal validation was carried out in a health clinic in Lima (Peru) with data obtained from an information system that supports the surgery block process. Finally, the results contribute to the optimization of decision-making by the medical staff involved in the surgery block process.
Revisión por pares
Hackl, Peter, and Michaela Denk. "Data Integration: Techniques and Evaluation." Austrian Statistical Society, 2004. http://epub.wu.ac.at/5631/1/435%2D1317%2D1%2DSM.pdf.
Повний текст джерелаMiranda, Inês Brás de Moura Duarte. "KPIs as a measure for quality in master data." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14590.
Повний текст джерелаNuma época em que as empresas estão a dar mais e mais importância à implementação de sistemas de informação para suportar o seu negócio, o termo Master Data está a tornar-se mais usual uma vez que concerne a informação mais importante de uma empresa (p.e. dados de clientes e colaboradores). Manter níveis elevados de qualidade para estes dados é um desafio que precisa de ser medido através de indicadores de performance (p.e. Key Performance Indicators). O presente estudo tem o objetivo de investigar a definição, cálculo, divulgação e uso de Key Performance Indicators numa empresa multinacional. Para este propósito, uma formação de 6 meses foi providenciada pela empresa para explicar como calcular estes valores e como obter toda a informação necessária relativamente a estes indicadores dentro do departamento de Recursos Humanos. A análise mostrou que, apesar dos KPIs existentes estarem bem definidos e serem bem calculados, não são suficientes para incluir todas as classes de master data e são também muito abrangentes, tornando quase impossível que seja encontrada a raíz do problema dos RH na empresa.
In a time where companies are giving more and more importance to the implementation of information systems to support their businesses, the term Master Data is becoming more usual since it concerns the core information of a company (e.g. customer and employee data). Maintaining the highest quality for this data is nevertheless a challenge that needs to be measured through performance measures (for example: Key Performance Indicators). The present case study has the purpose of investigating the definition, calculation, divulgation and use of Key Performance Indicators within a multinational company. To this end, a training of 6 months was provided by the company to teach participants how to calculate these values and also how to obtain all the necessary information regarding these indicators within the Human Resources department. The analysis showed that, even though the existing KPIs are well defined and calculated, they are not enough to include all existing master data classes and are also too wide-ranging, making it almost impossible to find the root of the HR problems within the company.
info:eu-repo/semantics/publishedVersion
Breuler, Lindsay Mildred. "Developing Ohio 4-H Horse Project Quality Indicators through the Analysis of Enrollment Data and Volunteer Leader Discourse: A Mixed Model Approach." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429549736.
Повний текст джерелаCampbell, William W. III. "A COMPARISON OF QUALITY INDICATORS BETWEEN MEDICARE ACCOUNTABLE CARE ORGANIZATIONS AND HEALTH MAINTENANCE ORGANIZATIONS USING PUBLICLY AVAILABLE DATA." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5284.
Повний текст джерелаBentley, Tabitha Anne. "Performance Improvement Data and Staff Responsibility." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3485.
Повний текст джерелаElefante, Elena. "Integrating Patient Reported Outcomes, clinical data and quality indicators to physician driven data in clinical management of chronic rheumatic diseases: the paradigm of Systemic Lupus Erythematosus (SLE)." Doctoral thesis, Università di Siena, 2021. http://hdl.handle.net/11365/1160790.
Повний текст джерелаКниги з теми "Data quality indicators"
Bank, World. The development data book: A guide to social and economic statistics : with a comprehensive data table. 3rd ed. Washington, D.C: World Bank, 1995.
Знайти повний текст джерелаAssociation, American Nurses', ed. Nursing quality indicators: Guide for implementation. 2nd ed. Washington, D.C: American Nurses Association (600 Maryland Ave., S.W., Washington D.C. 20024-2571), 1999.
Знайти повний текст джерелаUnited Nations Research Institute for Social Development., ed. Qualitative indicators and development data: Current concerns and priorities. Geneva, Switzerland: The Institute, 1991.
Знайти повний текст джерелаPopulation Research Centre (Institute for Social and Economic Change). Assessing the quality of district data for improved planning and monitoring of development programmes. New Delhi: United Nations Population Fund, 2011.
Знайти повний текст джерелаUnited States. Forest Service. Northern Research Station, ed. FIA national assessment of data quality for forest health indicators. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station, 2009.
Знайти повний текст джерелаStanton, Cynthia. DHS maternal mortality indicators: An assessment of data quality and implications for data use. Calverton: Macro International, 1997.
Знайти повний текст джерелаNoureddine, Abderrahim, Hill Ken 1945-, and Macro International. Institute for Resource Development. Demographic and Health Surveys, eds. DHS maternal mortality indicators: An assessment of data quality and implications for data use. Calverton, Md: Macro International, Inc., 1997.
Знайти повний текст джерелаOffice, General Accounting. Vietnam economic data: Assessment of availability and quality : report to Congressional requesters. Washington, D.C. (P.O. Box 37050 Washington, D.C. 20013): The Office, 1999.
Знайти повний текст джерелаNezlek, John B. Community health & resource data guide 2000. [Richmond, Va: Dept. of Health, 2000.
Знайти повний текст джерелаCommission, European, and Statistical Office of the European Communities., eds. Living conditions in Europe: Statistical pocketbook : data 1998-2002. 2nd ed. Luxembourg: Office for Official Publications of the European Communities, 2004.
Знайти повний текст джерелаЧастини книг з теми "Data quality indicators"
Kiatkajitmun, Pranungwad, Chanwit Chanton, Pairach Piboonrungroj, and Juggapong Natwichai. "Data Quality Assessment Framework and Economic Indicators." In Advances in Networked-based Information Systems, 97–105. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40978-3_11.
Повний текст джерелаKąkol, Krzysztof, Gražina Korvel, and Bożena Kostek. "Improving Objective Speech Quality Indicators in Noise Conditions." In Data Science: New Issues, Challenges and Applications, 199–218. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39250-5_11.
Повний текст джерелаDoran, John W., and Timothy B. Parkin. "Quantitative Indicators of Soil Quality: A Minimum Data Set." In SSSA Special Publications, 25–37. Madison, WI, USA: Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub49.c2.
Повний текст джерелаPrabhakar, Deepak, Raquel Y. Qualls-Hampton, Rachael Jackson, and Kathryn M. Cardarelli. "Mental Health Indicator Parity: Integrating National, State, and Local Data." In Community Quality-of-Life Indicators: Best Cases IV, 81–109. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2243-1_4.
Повний текст джерелаLópez-Mesa, Belinda, Carlos Beltrán-Velamazán, Marta Gómez-Gil, Marta Monzón-Chavarrías, and Almudena Espinosa-Fernández. "New Approaches to Generate Data to Measure the Progress of Decarbonization of the Building Stock in Europe and Spain." In Digital Innovations in Architecture, Engineering and Construction, 317–46. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51829-4_12.
Повний текст джерелаWess, Raphael, Heiner Klock, Hans-Stefan Siller, and Gilbert Greefrath. "Test Quality." In International Perspectives on the Teaching and Learning of Mathematical Modelling, 77–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78071-5_4.
Повний текст джерелаFattore, Marco, Filomena Maggino, and Emilio Colombo. "From Composite Indicators to Partial Orders: Evaluating Socio-Economic Phenomena Through Ordinal Data." In Quality of life in Italy, 41–68. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3898-0_4.
Повний текст джерелаGómez-Gil, Marta, Markel Arbulu, Rufino J. Hernández-Minguillón, and Belinda López-Mesa. "On the Availability and Quality of Data in Spain for the Development of Indicators to Measure Building Renovation Policies Effectiveness and the Decarbonization of the Building Stock." In Digital Innovations in Architecture, Engineering and Construction, 291–316. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51829-4_11.
Повний текст джерелаRidzi, Frank. "Goldilocks Data-Connecting Community Indicators to Program Evaluation and Everything in Between." In Community Quality-of-Life and Well-Being, 15–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48182-7_2.
Повний текст джерелаOl’ha, Bashyns’ka, Kazymyr Volodymyr, Nesterenko Sergii, and Olga Prila. "Dynamic Assessment of the UAS Quality Indicators by Technical Diagnostics Data." In Advances in Intelligent Systems and Computing, 154–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25741-5_16.
Повний текст джерелаТези доповідей конференцій з теми "Data quality indicators"
Brook, Anna, and Eyal Ben Dor. "Spectral quality indicators for hyperspectral data." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080934.
Повний текст джерелаMarev, Milen, Ernesto Compatangelo, and Wamberto Vasconcelos. "Intrinsic Indicators for Numerical Data Quality." In 5th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009411403410348.
Повний текст джерела"Data Quality Sensitivity Analysis on Aggregate Indicators." In International Conference on Data Technologies and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004040300970108.
Повний текст джерелаWenlu Yang, Alzennyr Da Silva, and Marie-Luce Picard. "Computing data quality indicators on Big Data streams using a CEP." In 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). IEEE, 2015. http://dx.doi.org/10.1109/iwcim.2015.7347061.
Повний текст джерелаVlčková, Miroslava. "Dependency of Accounting Data Quality on Selected Financial Indicators." In Hradec Economic Days 2018, edited by Petra Maresova, Pavel Jedlicka, and Ivan Soukal. University of Hradec Kralove, 2018. http://dx.doi.org/10.36689/uhk/hed/2018-02-043.
Повний текст джерелаWimalasena, NN, A. Chang-Richards, KIK Wang, and K. Dirks. "Housing quality indicators: A systematic review." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.43.
Повний текст джерелаBedzsula, Balint. "QUALITY IMPROVEMENT IN HIGHER EDUCATION BASED ON DATA AND INDICATORS." In 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. Stef92 Technology, 2015. http://dx.doi.org/10.5593/sgemsocial2015/b12/s3.102.
Повний текст джерелаLazzaris, Joana, André M. Carvalho, and Maria Sameiro Carvalho. "Towards Data-Driven, Sustainable Supply Chain Quality Management 5.0 Indicators." In 6th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2023. http://dx.doi.org/10.46254/eu6.20230113.
Повний текст джерелаJašková, Dana. "Development of Human Capital Quality Based on Quantitative Indicators." In 6th International Scientific Conference – EMAN 2022 – Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2022. http://dx.doi.org/10.31410/eman.2022.145.
Повний текст джерелаRonné, Jules, Laura Dubuis, and Thomas Robert. "Assessment of bicycle experimental objective handling quality indicators." In The Evolving Scholar - BMD 2023, 5th Edition. The Evolving Scholar - BMD 2023, 5th Edition, 2023. http://dx.doi.org/10.59490/6504c0e90df003ee2fc2a2e0.
Повний текст джерелаЗвіти організацій з теми "Data quality indicators"
Westfall, James A. FIA national assessment of data quality for forest health indicators. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2009. http://dx.doi.org/10.2737/nrs-gtr-53.
Повний текст джерелаRoux, Anne M., Jessica E. Rast, K. A. Anderson, and Paul T. Shattuck. National Autism Indicators Report: Vocational Rehab. A.J. Drexel Autism Institute, May 2016. http://dx.doi.org/10.17918/nairvocrehab2016.
Повний текст джерелаVos, Rob. Educational Indicators: What's to Be Measured? Inter-American Development Bank, January 1996. http://dx.doi.org/10.18235/0011588.
Повний текст джерелаAsfaw, Etenesh, and Bakhrom Mirkasimov. Tracking Green Growth Indicators for Uzbekistan: A first stocktaking exercise-2023. TOSHKENT SHAHRIDAGI XALQARO VESTMINSTER UNIVERSITETI, April 2024. https://doi.org/10.70735/mulu8653.
Повний текст джерелаHilgert, Marianne, and Miguel Székely. What's Behind the Inequality We Measure: An Investigation Using Latin American Data. Inter-American Development Bank, December 1999. http://dx.doi.org/10.18235/0010769.
Повний текст джерелаRast, Jessica E., Kaitlin H. Koffer Miller, Julianna Rava, Jonas C. Ventimiglia, Sha Tao, Jennifer Bromberg, Jennifer L. Ames, Lisa A. Croen, Alice Kuo, and Lindsay L. Shea. National Autism Indicators Report: Health and the COVID-19 Pandemic: July 2023. A.J. Drexel Autism Institute, 2023. http://dx.doi.org/10.17918/covidnair2023.
Повний текст джерелаAnilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil, and G. A. Wainer. Canadian jobs amid a pandemic : examining the relationship between professional industry and salary to regional key performance indicators. Department of Systems and Computer Engineering, Carleton University, June 2022. http://dx.doi.org/10.22215/dsce/220608.
Повний текст джерелаWeissinger, Rebecca, and Carolyn Hackbarth. Water quality in the Northern Colorado Plateau Network: Water years 2019?2022. National Park Service, 2024. http://dx.doi.org/10.36967/2304433.
Повний текст джерелаBoix, Carles, Alícia Adserà, and J. Mark Payne. Are You Being Served?: Political Accountability and Quality of Government. Inter-American Development Bank, November 2000. http://dx.doi.org/10.18235/0010787.
Повний текст джерелаLi, wanlin, jie Yun, siying He, ziqi Zhou, and ling He. Effect of different exercise therapies on fatigue in maintenance hemodialysis patients:A Bayesian Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0144.
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