Literatura académica sobre el tema "Data quality indicators"
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Artículos de revistas sobre el tema "Data quality indicators"
Matute, Jorge y A. P. Gupta. "Data Quality and Indicators". American Journal of Agricultural and Biological Sciences 2, n.º 1 (1 de enero de 2007): 23–30. http://dx.doi.org/10.3844/ajabssp.2007.23.30.
Texto completoJohnson, Barry L., T. Damstra, Chris Derosa, C. Elmer y M. Gilbert. "Workshop on Toxicological Data Quality Indicators". Toxicology and Industrial Health 9, n.º 4 (julio de 1993): 577–604. http://dx.doi.org/10.1177/074823379300900402.
Texto completoIseni, Jeton y 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 (septiembre de 2016): A70.2—A70. http://dx.doi.org/10.1136/jnnp-2016-314597.198.
Texto completoWeiss, Manfred S. "Global indicators of X-ray data quality". Journal of Applied Crystallography 34, n.º 2 (1 de abril de 2001): 130–35. http://dx.doi.org/10.1107/s0021889800018227.
Texto completoCadarette, S. M., S. B. Jaglal, L. Raman-Wilms, D. E. Beaton y J. M. Paterson. "Osteoporosis quality indicators using healthcare utilization data". Osteoporosis International 22, n.º 5 (25 de junio de 2010): 1335–42. http://dx.doi.org/10.1007/s00198-010-1329-8.
Texto completoWeiss, M. S. y R. Hilgenfeld. "Global Indicators of X-ray Data Quality". Acta Crystallographica Section A Foundations of Crystallography 56, s1 (25 de agosto de 2000): s105. http://dx.doi.org/10.1107/s0108767300022789.
Texto completoPulido Moncada, Mansonia, Donald Gabriels y Wim M. Cornelis. "Data-driven analysis of soil quality indicators using limited data". Geoderma 235-236 (diciembre de 2014): 271–78. http://dx.doi.org/10.1016/j.geoderma.2014.07.014.
Texto completoPickering, Ashley E., Petrus Malherbe, Joan Nambuba, Corey B. Bills, Emilie Calvello Hynes y Brian Rice. "Clinical emergency care quality indicators in Africa: a scoping review and data summary". BMJ Open 13, n.º 5 (mayo de 2023): e069494. http://dx.doi.org/10.1136/bmjopen-2022-069494.
Texto completoKolozsvári, László Róbert y Imre Rurik. "Quality improvement in primary care. Financial incentives related to quality indicators in Europe". Orvosi Hetilap 154, n.º 28 (julio de 2013): 1096–101. http://dx.doi.org/10.1556/oh.2013.29631.
Texto completoSchnelle, John F., Mary P. Cadogan, June Yoshii, Nahla R. Al-Samarrai, Dan Osterweil, Barbara M. Bates-Jensen y Sandra F. Simmons. "The Minimum Data Set Urinary Incontinence Quality Indicators". Medical Care 41, n.º 8 (agosto de 2003): 909–22. http://dx.doi.org/10.1097/00005650-200308000-00005.
Texto completoTesis sobre el tema "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.
Texto completoTime 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.
Texto completoNtshuntshe-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.
Texto completoThis 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 y 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.
Texto completoIn 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 y Michaela Denk. "Data Integration: Techniques and Evaluation". Austrian Statistical Society, 2004. http://epub.wu.ac.at/5631/1/435%2D1317%2D1%2DSM.pdf.
Texto completoMiranda, 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.
Texto completoNuma 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.
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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.
Texto completoCampbell, 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.
Texto completoBentley, Tabitha Anne. "Performance Improvement Data and Staff Responsibility". ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3485.
Texto completoElefante, 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.
Texto completoLibros sobre el tema "Data quality indicators"
Bank, World. The development data book: A guide to social and economic statistics : with a comprehensive data table. 3a ed. Washington, D.C: World Bank, 1995.
Buscar texto completoAssociation, American Nurses', ed. Nursing quality indicators: Guide for implementation. 2a ed. Washington, D.C: American Nurses Association (600 Maryland Ave., S.W., Washington D.C. 20024-2571), 1999.
Buscar texto completoUnited Nations Research Institute for Social Development., ed. Qualitative indicators and development data: Current concerns and priorities. Geneva, Switzerland: The Institute, 1991.
Buscar texto completoPopulation 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.
Buscar texto completoUnited 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.
Buscar texto completoStanton, Cynthia. DHS maternal mortality indicators: An assessment of data quality and implications for data use. Calverton: Macro International, 1997.
Buscar texto completoNoureddine, Abderrahim, Hill Ken 1945- y 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.
Buscar texto completoOffice, 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.
Buscar texto completoNezlek, John B. Community health & resource data guide 2000. [Richmond, Va: Dept. of Health, 2000.
Buscar texto completoCommission, European y Statistical Office of the European Communities., eds. Living conditions in Europe: Statistical pocketbook : data 1998-2002. 2a ed. Luxembourg: Office for Official Publications of the European Communities, 2004.
Buscar texto completoCapítulos de libros sobre el tema "Data quality indicators"
Kiatkajitmun, Pranungwad, Chanwit Chanton, Pairach Piboonrungroj y Juggapong Natwichai. "Data Quality Assessment Framework and Economic Indicators". En 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.
Texto completoKąkol, Krzysztof, Gražina Korvel y Bożena Kostek. "Improving Objective Speech Quality Indicators in Noise Conditions". En 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.
Texto completoDoran, John W. y Timothy B. Parkin. "Quantitative Indicators of Soil Quality: A Minimum Data Set". En SSSA Special Publications, 25–37. Madison, WI, USA: Soil Science Society of America, 2015. http://dx.doi.org/10.2136/sssaspecpub49.c2.
Texto completoPrabhakar, Deepak, Raquel Y. Qualls-Hampton, Rachael Jackson y Kathryn M. Cardarelli. "Mental Health Indicator Parity: Integrating National, State, and Local Data". En 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.
Texto completoLópez-Mesa, Belinda, Carlos Beltrán-Velamazán, Marta Gómez-Gil, Marta Monzón-Chavarrías y Almudena Espinosa-Fernández. "New Approaches to Generate Data to Measure the Progress of Decarbonization of the Building Stock in Europe and Spain". En 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.
Texto completoWess, Raphael, Heiner Klock, Hans-Stefan Siller y Gilbert Greefrath. "Test Quality". En 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.
Texto completoFattore, Marco, Filomena Maggino y Emilio Colombo. "From Composite Indicators to Partial Orders: Evaluating Socio-Economic Phenomena Through Ordinal Data". En Quality of life in Italy, 41–68. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3898-0_4.
Texto completoGómez-Gil, Marta, Markel Arbulu, Rufino J. Hernández-Minguillón y 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". En 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.
Texto completoRidzi, Frank. "Goldilocks Data-Connecting Community Indicators to Program Evaluation and Everything in Between". En 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.
Texto completoOl’ha, Bashyns’ka, Kazymyr Volodymyr, Nesterenko Sergii y Olga Prila. "Dynamic Assessment of the UAS Quality Indicators by Technical Diagnostics Data". En 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.
Texto completoActas de conferencias sobre el tema "Data quality indicators"
Brook, Anna y Eyal Ben Dor. "Spectral quality indicators for hyperspectral data". En 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.
Texto completoMarev, Milen, Ernesto Compatangelo y Wamberto Vasconcelos. "Intrinsic Indicators for Numerical Data Quality". En 5th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009411403410348.
Texto completo"Data Quality Sensitivity Analysis on Aggregate Indicators". En International Conference on Data Technologies and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004040300970108.
Texto completoWenlu Yang, Alzennyr Da Silva y Marie-Luce Picard. "Computing data quality indicators on Big Data streams using a CEP". En 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). IEEE, 2015. http://dx.doi.org/10.1109/iwcim.2015.7347061.
Texto completoVlčková, Miroslava. "Dependency of Accounting Data Quality on Selected Financial Indicators". En Hradec Economic Days 2018, editado por Petra Maresova, Pavel Jedlicka y Ivan Soukal. University of Hradec Kralove, 2018. http://dx.doi.org/10.36689/uhk/hed/2018-02-043.
Texto completoWimalasena, NN, A. Chang-Richards, KIK Wang y K. Dirks. "Housing quality indicators: A systematic review". En 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.43.
Texto completoBedzsula, Balint. "QUALITY IMPROVEMENT IN HIGHER EDUCATION BASED ON DATA AND INDICATORS". En 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. Stef92 Technology, 2015. http://dx.doi.org/10.5593/sgemsocial2015/b12/s3.102.
Texto completoLazzaris, Joana, André M. Carvalho y Maria Sameiro Carvalho. "Towards Data-Driven, Sustainable Supply Chain Quality Management 5.0 Indicators". En 6th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2023. http://dx.doi.org/10.46254/eu6.20230113.
Texto completoJašková, Dana. "Development of Human Capital Quality Based on Quantitative Indicators". En 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.
Texto completoRonné, Jules, Laura Dubuis y Thomas Robert. "Assessment of bicycle experimental objective handling quality indicators". En The Evolving Scholar - BMD 2023, 5th Edition. The Evolving Scholar - BMD 2023, 5th Edition, 2023. http://dx.doi.org/10.59490/6504c0e90df003ee2fc2a2e0.
Texto completoInformes sobre el tema "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.
Texto completoRoux, Anne M., Jessica E. Rast, K. A. Anderson y Paul T. Shattuck. National Autism Indicators Report: Vocational Rehab. A.J. Drexel Autism Institute, mayo de 2016. http://dx.doi.org/10.17918/nairvocrehab2016.
Texto completoVos, Rob. Educational Indicators: What's to Be Measured? Inter-American Development Bank, enero de 1996. http://dx.doi.org/10.18235/0011588.
Texto completoAsfaw, Etenesh y Bakhrom Mirkasimov. Tracking Green Growth Indicators for Uzbekistan: A first stocktaking exercise-2023. TOSHKENT SHAHRIDAGI XALQARO VESTMINSTER UNIVERSITETI, abril de 2024. https://doi.org/10.70735/mulu8653.
Texto completoHilgert, Marianne y Miguel Székely. What's Behind the Inequality We Measure: An Investigation Using Latin American Data. Inter-American Development Bank, diciembre de 1999. http://dx.doi.org/10.18235/0010769.
Texto completoRast, Jessica E., Kaitlin H. Koffer Miller, Julianna Rava, Jonas C. Ventimiglia, Sha Tao, Jennifer Bromberg, Jennifer L. Ames, Lisa A. Croen, Alice Kuo y 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.
Texto completoAnilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil y 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, junio de 2022. http://dx.doi.org/10.22215/dsce/220608.
Texto completoWeissinger, Rebecca y 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.
Texto completoBoix, Carles, Alícia Adserà y J. Mark Payne. Are You Being Served?: Political Accountability and Quality of Government. Inter-American Development Bank, noviembre de 2000. http://dx.doi.org/10.18235/0010787.
Texto completoLi, wanlin, jie Yun, siying He, ziqi Zhou y 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, noviembre de 2022. http://dx.doi.org/10.37766/inplasy2022.11.0144.
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