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Статті в журналах з теми "Data Importance"
Hejazi, Aylin, Neda Abdolvand, and Saeedeh Rajaee Harandi. "Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area." International Journal of Trade, Economics and Finance 8, no. 2 (April 2017): 102–8. http://dx.doi.org/10.18178/ijtef.2017.8.2.547.
Повний текст джерелаBrown, Renée F. "The Importance of Data Citation." BioScience 71, no. 3 (March 1, 2021): 211. http://dx.doi.org/10.1093/biosci/biab012.
Повний текст джерелаSpetz, Joanne. "The Importance of Good Data." Annual Review of Nursing Research 28, no. 1 (December 2010): 1–18. http://dx.doi.org/10.1891/0739-6686.28.1.
Повний текст джерелаNickels, Liz. "The importance of big data." Metal Powder Report 74, no. 4 (July 2019): 181–83. http://dx.doi.org/10.1016/j.mprp.2019.05.003.
Повний текст джерелаWold, Svante, and Paul Geladi. "The importance of raw data." Chemometrics and Intelligent Laboratory Systems 22, no. 1 (January 1994): 1–2. http://dx.doi.org/10.1016/0169-7439(93)e0046-7.
Повний текст джерелаSamuelson, Frank, Brandon D. Gallas, Kyle J. Myers, Nicholas Petrick, Paul Pinsky, Berkman Sahiner, Gregory Campbell, and Gene A. Pennello. "The Importance of ROC Data." Academic Radiology 18, no. 2 (February 2011): 257–58. http://dx.doi.org/10.1016/j.acra.2010.10.016.
Повний текст джерелаJang, Kyoung-Ae, and Woo-Je Kim. "Component Development and Importance Weight Analysis of Data Governance." Journal of the Korean Operations Research and Management Science Society 41, no. 3 (August 31, 2016): 45–58. http://dx.doi.org/10.7737/jkorms.2016.41.3.045.
Повний текст джерелаRichardson, Julie, and Diane Hoffman-Kim. "The Importance of Defining ‘Data’ in Data Management Policies." Science and Engineering Ethics 16, no. 4 (September 19, 2010): 749–51. http://dx.doi.org/10.1007/s11948-010-9231-5.
Повний текст джерелаChaoli Wang, Hongfeng Yu, and Kwan-Liu Ma. "Importance-Driven Time-Varying Data Visualization." IEEE Transactions on Visualization and Computer Graphics 14, no. 6 (November 2008): 1547–54. http://dx.doi.org/10.1109/tvcg.2008.140.
Повний текст джерелаJollis, James G. "Coronary Revascularization: Importance of Observational Data." Mayo Clinic Proceedings 71, no. 10 (October 1996): 1016–17. http://dx.doi.org/10.1016/s0025-6196(11)63780-4.
Повний текст джерелаДисертації з теми "Data Importance"
Törnqvist, Christian. "Evaluating the Importance of Disk-locality for Data Analytics Workloads : Evaluating the Importance of Disk-locality for Data Analytics Workloads." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-410212.
Повний текст джерелаStephens, Joshua J. "Data Governance Importance and Effectiveness| Health System Employee Perception." Thesis, Central Michigan University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751061.
Повний текст джерелаThe focus of this study was to understand how health system employees define Data Governance (DG), how they perceive its importance and effectiveness to their role and how it may impact strategic outcomes of the organization. Having a better understanding of employee perceptions will help identify areas of education, process improvement and opportunities for more structured data governance within the healthcare industry. Additionally, understanding how employees associate each of these domains to strategic outcomes, will help inform decision-makers on how best to align the Data Governance strategy with that of the organization.
This research is intended to expand the data governance community’s knowledge about how health system employee demographics influence their perceptions of Data Governance. Very little academic research has been done to-date, which is unfortunate given the value of employee engagement to an organization’s culture juxtaposed to the intent of Data Governance to change that culture into one that fully realizes the value of its data and treats it as a corporate asset. This lack of understanding leads to two distinct problems: executive resistance toward starting a Data Governance Program due to the lack of association between organizational strategic outcomes and Data Governance, and employee, or cultural, resistance to the change Data Governance brings to employee roles and processes.
The dataset for this research was provided by a large mid-west health system’s Enterprise Data Governance Program and was collected internally through an electronic survey. A mixed methods approach was taken. The first analysis intended to see how employees varied in their understanding of the definition of data governance as represented by the Data Management Association’s DAMA Wheel. The last three research questions focused on determining which factors influence a health system employee’s perception of the importance, effectiveness, and impact Data Governance has on their role and on the organization.
Perceptions on the definition of Data Governance varied slightly for Gender, Management Role, IT Role, and Role Tenure, and the thematic analysis identified a lack of understanding of Data Governance by health system employees. Perceptions of Data Governance importance and effectiveness varied by participants’ gender, and organizational role as part of analytics, IT, and Management. In general, employees perceive a deficit of data governance to their role based on their perceptions of importance and effectiveness. Lastly, employee perceptions of the impact of Data Governance on strategic outcomes varied among participants by gender for Cost of Care and by Analytics Role for Quality of Analytics. For both Quality of Care and Patient Experience, perceptions did not vary.
Perceptions related to the impact of Data Governance on strategic outcomes found that Data Quality Management was most impactful to all four strategic outcomes included in the study: quality of care, cost of care, patient experience, and quality of analytics. Leveraging the results of this study to tailor communication, education and training, and roles and responsibilities required for a successful implementation of Data Governance in healthcare should be considered by DG practitioners and executive leadership implementing or evaluating a DG Program within a healthcare organization. Additionally, understanding employee perceptions of Data Governance and their impact to strategic outcomes will provide meaningful insight to executive leadership who have difficulty connecting the cost of Data Governance to the value realization, which is moving the organization closer to achieving the Triple Aim by benefiting from their data.
Bordoloi, Udeepta Dutta. "Importance-driven algorithms for scientific visualization." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1118952958.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xiv, 126 p.; also includes graphics. Includes bibliographical references (p. 119-126). Available online via OhioLINK's ETD Center
Northrop, Amanda Rosalind. "Importance of various data sources in deterministic stock assessment models." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1002811.
Повний текст джерелаWan, Shuyan. "Likelihood-based procedures for obtaining confidence intervals of disease Loci with general pedigree data." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1164815591.
Повний текст джерелаMatthäus, Antje, and Markus Dammers. "Computational underground short-term mine planning: the importance of real-time data." Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-231345.
Повний текст джерелаMafu, Thandile John. "Modelling of multi-state panel data : the importance of the model assumptions." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95994.
Повний текст джерелаENGLISH ABSTRACT: A multi-state model is a way of describing a process in which a subject moves through a series of states in continuous time. The series of states might be the measurement of a disease for example in state 1 we might have subjects that are free from disease, in state 2 we might have subjects that have a disease but the disease is mild, in state 3 we might have subjects having a severe disease and in last state 4 we have those that die because of the disease. So Markov models estimates the transition probabilities and transition intensity rates that describe the movement of subjects between these states. The transition might be for example a particular subject or patient might be slightly sick at age 30 but after 5 years he or she might be worse. So Markov model will estimate what probability will be for that patient for moving from state 2 to state 3. Markov multi-state models were studied in this thesis with the view of assessing the Markov models assumptions such as homogeneity of the transition rates through time, homogeneity of the transition rates across the subject population and Markov property or assumption. The assessments of these assumptions were based on simulated panel or longitudinal dataset which was simulated using the R package named msm package developed by Christopher Jackson (2014). The R code that was written using this package is attached as appendix. Longitudinal dataset consists of repeated measurements of the state of a subject and the time between observations. The period of time with observations in longitudinal dataset is being made on subject at regular or irregular time intervals until the subject dies then the study ends.
AFRIKAANSE OPSOMMING: ’n Meertoestandmodel is ’n manier om ’n proses te beskryf waarin ’n subjek in ’n ononderbroke tydperk deur verskeie toestande beweeg. Die verskillende toestande kan byvoorbeeld vir die meting van siekte gebruik word, waar toestand 1 uit gesonde subjekte bestaan, toestand 2 uit subjekte wat siek is, dog slegs matig, toestand 3 uit subjekte wat ernstig siek is, en toestand 4 uit subjekte wat aan die siekte sterf. ’n Markov-model raam die oorgangswaarskynlikhede en -intensiteit wat die subjekte se vordering deur hierdie toestande beskryf. Die oorgang is byvoorbeeld wanneer ’n bepaalde subjek of pasiënt op 30-jarige ouderdom net lig aangetas is, maar na vyf jaar veel ernstiger siek is. Die Markov-model raam dus die waarskynlikheid dat so ’n pasiënt van toestand 2 tot toestand 3 sal vorder. Hierdie tesis het ondersoek ingestel na Markov-meertoestandmodelle ten einde die aannames van die modelle, soos die homogeniteit van oorgangstempo’s oor tyd, die homogeniteit van oorgangstempo’s oor die subjekpopulasie en tipiese Markov-eienskappe, te beoordeel. Die beoordeling van hierdie aannames was gegrond op ’n gesimuleerde paneel of longitudinale datastel wat met behulp van Christopher Jackson (2014) se R-pakket genaamd msm gesimuleer is. Die R-kode wat met behulp van hierdie pakket geskryf is, word as bylae aangeheg. Die longitudinale datastel bestaan uit herhaalde metings van die toestand waarin ’n subjek verkeer en die tydsverloop tussen waarnemings. Waarnemings van die longitudinale datastel word met gereelde of ongereelde tussenposes onderneem totdat die subjek sterf, wanneer die studie dan ook ten einde loop.
Matthäus, Antje, and Markus Dammers. "Computational underground short-term mine planning: the importance of real-time data." TU Bergakademie Freiberg, 2017. https://tubaf.qucosa.de/id/qucosa%3A23194.
Повний текст джерелаKaponen, Martina. "Fairness and parameter importance in logistic regression models of criminal sentencing data." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417359.
Повний текст джерелаNalini, Ramakrishna Sindhu Kanya. "Component importance indices and failure prevention using outage data in distribution systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287173.
Повний текст джерелаAvbrott i strömförsörjningen är oundvikliga på grund av fel i distributionsnätet för kraftsystemet. Dessa avbrott är inte bara dyra för kunderna utan också för distributionssystemoperatören i form av påföljder. Ökad systemredundans eller användning av komponentspecifika sensorer kan hjälpa till att minska avbrott. Dessa alternativ är dock inte alltid ekonomiskt genomförbara. Därför är det nödvändigt att kontrollera om det finns andra möjligheter för att minska risken för avbrott. Data lagrade i transformatorstationer kan användas för att minska risken för avbrott genom att härleda komponentviktindex följt av rangordning och förutsäga avbrott. I denna avhandling härleds viktighetsindex genom att identifiera de kritiska komponenterna i nätet och tilldela index baserat på vissa kriterier. Felprognoserna gjordes baserat på de väderförhållanden som observerades under avbrott. komponentviktighetsindex härleds och rankas baserat på komponenternas urladdningstid, frekvens och påverkan av avbrott. Detta hjälper till att prioritera komponenter enligt det valda kriteriet och anpassa övervakningsstrategier genom att fokusera på de mest kritiska komponenterna. Baserat på kategoriska Naive Bayes utvecklas en modell för att förutsäga sannolikheten för fel / fel, plats och komponenttyp som sannolikt kommer att påverkas under en viss uppsättning väderförhållanden. Resultaten från komponentviktighetsindexen visar att varje komponents rang varierar beroende på det valda kriteriet. Vissa komponenter rankas dock högt i alla metoder. Dessa komponenter är kritiska och behöver fokuserad övervakning. Tillförlitligheten hos resultat från komponentviktindex beror till stor del på tidsramen för avbrottsdata som beaktas för analys. Prognosmodellen kan varna distributionssystemoperatören om möjliga avbrott i nätverket för en viss uppsättning väderförhållanden. Förutsägelsen av plats och komponenttyp som sannolikt kommer att påverkas är dock relativt felaktig, eftersom antalet avbrott som beaktas i tidsramen är lågt. Genom att uppdatera modellen regelbundet med nya data skulle förutsägelserna vara mer exakta.
Книги з теми "Data Importance"
Euclid, ed. Dedomena: Euclid's Data, or, The importance of being given. Copenhagen: Museum Tusculanum Press, 2003.
Знайти повний текст джерелаComin, Diego. Using investment data to assess the importance of price mismeasurement. Cambridge, MA: National Bureau of Economic Research, 2004.
Знайти повний текст джерелаGoldstein, David K. Computer-based data and organizational learning: The importance of managers' stories. Cambridge, Mass: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology, 1992.
Знайти повний текст джерелаPatterson, K. D. Modelling the Livingston price expectations data: The importance of monthly information. Reading: University of Reading Department of Economics, 1985.
Знайти повний текст джерелаMaclean, Alex. Analysing the importance of wireless technology andinvestigating data transmission over wireless networks. Oxford: Oxford Brookes University, 2001.
Знайти повний текст джерелаMoshirpour, Mohammad, Behrouz Far, and Reda Alhajj, eds. Highlighting the Importance of Big Data Management and Analysis for Various Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60255-4.
Повний текст джерелаMairesse, Jacques. The importance of R & D for innovation: A reassesment using French survey data. Cambridge, MA: National Bureau of Economic Research, 2004.
Знайти повний текст джерелаMcCardell, Orla. The importance of building maintenance and the availablity of building maintenance cost data. [s.l: The Author], 1997.
Знайти повний текст джерелаMairesse, Jacques. The importance of R & D for innovation: A reassessment using French survey data. Cambridge, Mass: National Bureau of Economic Research, 2004.
Знайти повний текст джерелаSlawson, Nadine. The value and importance of differentiation when teaching handling data to junior age children. Cardiff: CIHE, 1995.
Знайти повний текст джерелаЧастини книг з теми "Data Importance"
Wagh, Sanjeev J., Manisha S. Bhende, and Anuradha D. Thakare. "Importance of Data Science." In Fundamentals of Data Science, 3–15. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429443237-2.
Повний текст джерелаRuggles, Steven. "The Importance of Data Curation." In The Palgrave Handbook of Survey Research, 303–8. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54395-6_39.
Повний текст джерелаAlford, Samantha. "The importance of data protection." In Quality Management Systems, 167–93. Milton Park, Abingdon, Oxon ; New York : Routledge, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9780429274473-8.
Повний текст джерелаLink, Daniel. "Match Importance Affects Player Activity." In Data Analytics in Professional Soccer, 71–84. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-21177-6_5.
Повний текст джерелаZhao, Lu, Li Xiong, and Shan Xue. "Global Recursive Based Node Importance Evaluation." In Advanced Data Mining and Applications, 738–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_53.
Повний текст джерелаZhang, Zherong, Wenge Rong, Yuanxin Ouyang, and Zhang Xiong. "Importance-Weighted Distance Aware Stocks Trend Prediction." In Web and Big Data, 357–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96890-2_30.
Повний текст джерелаDevine, Patrick W., C. A. Srinivasan, and Maliha S. Zaman. "Importance of Data in Decision-Making." In Business Intelligence Techniques, 21–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24700-5_2.
Повний текст джерелаJugulum, Rajesh. "Importance of Data Quality for Analytics." In Quality in the 21st Century, 23–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21332-3_2.
Повний текст джерелаFukuda, Shuichi. "Increasing Importance of Analog Data Processing." In Lecture Notes in Networks and Systems, 797–807. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10461-9_54.
Повний текст джерелаHe, Gongzhen, Junyong Luo, and Meijuan Yin. "An Evaluation Algorithm of the Importance of Network Node Based on Community Influence." In Data Mining and Big Data, 57–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7205-0_6.
Повний текст джерелаТези доповідей конференцій з теми "Data Importance"
Kiyomoto, Shinsaku, and Yutaka Miyake. "On Data Importance Analysis." In 2011 Third International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE, 2011. http://dx.doi.org/10.1109/incos.2011.127.
Повний текст джерелаAshby, G., and Y. Water. "Importance of High Quality Data." In IET Water Event 2013: Process Control and Automation. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0196.
Повний текст джерелаFaisal, S. M., G. Tziantzioulis, A. M. Gok, N. Hardavellas, S. Ogrenci-Memik, and S. Parthasarathy. "Edge importance identification for energy efficient graph processing." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363775.
Повний текст джерелаWaldschütz, Hannes, and Eva Hornecker. "The Importance of Data Curation for Data Physicalization." In DIS '20: Designing Interactive Systems Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3393914.3395892.
Повний текст джерелаHaque, Ahsanul, Swarup Chandra, Latifur Khan, and Charu Aggarwal. "Distributed Adaptive Importance Sampling on graphical models using MapReduce." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004280.
Повний текст джерелаAnagnostopoulos, Aris, Luca Becchetti, Adriano Fazzone, Ida Mele, and Matteo Riondato. "The Importance of Being Expert." In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2723722.
Повний текст джерелаProko, Eljona. "The Importance of Big Data Analytics." In University for Business and Technology International Conference. Pristina, Kosovo: University for Business and Technology, 2015. http://dx.doi.org/10.33107/ubt-ic.2015.89.
Повний текст джерелаProko, Eljona. "The Importance of Big Data Analytics." In University for Business and Technology International Conference. Pristina, Kosovo: University for Business and Technology, 2016. http://dx.doi.org/10.33107/ubt-ic.2016.2.
Повний текст джерелаTekieh, Mohammad Hossein, and Bijan Raahemi. "Importance of Data Mining in Healthcare." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809367.
Повний текст джерелаWalsh, J. W. "The Importance of Data-Software Interfaces." In Petroleum Industry Application of Microcomputers. Society of Petroleum Engineers, 1987. http://dx.doi.org/10.2118/16494-ms.
Повний текст джерелаЗвіти організацій з теми "Data Importance"
Johnson, Eric M., Robert Urquhart, and Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, June 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.
Повний текст джерелаComin, Diego. Using Investment Data to Assess the Importance of Price Mismeasurement. Cambridge, MA: National Bureau of Economic Research, July 2004. http://dx.doi.org/10.3386/w10627.
Повний текст джерелаBiersdorf, John, Ha Bui, Tatsuya Sakurahara, Seyed Reihani, Chris LaFleur, David Luxat, Steven Prescott, and Zahra Mohaghegh. Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1632319.
Повний текст джерелаMairesse, Jacques, and Pierre Mohnen. The Importance of R&D for Innovation: A Reassessment Using French Survey Data. Cambridge, MA: National Bureau of Economic Research, November 2004. http://dx.doi.org/10.3386/w10897.
Повний текст джерелаMatthew J. Tonkin, Claire R. Tiedeman, D. Matthew Ely, and and Mary C. Hill. OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions Using Linear Statistics. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/919524.
Повний текст джерелаBlau, Francine, and Lawrence Kahn. The Feasibility and Importance of Adding Measures of Actual Experience to Cross-Sectional Data Collection. Cambridge, MA: National Bureau of Economic Research, July 2011. http://dx.doi.org/10.3386/w17241.
Повний текст джерелаGlaser, R., G. Johannesson, S. Sengupta, B. Kosovic, S. Carle, G. Franz, R. Aines, et al. Stochastic Engine Final Report: Applying Markov Chain Monte Carlo Methods with Importance Sampling to Large-Scale Data-Driven Simulation. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/15009813.
Повний текст джерелаNchare, Karim. On the Importance of Functioning School Based Management Committees (SBMCs): Evidence from Nigeria. Research on Improving Systems of Education (RISE), November 2021. http://dx.doi.org/10.35489/bsg-rise-ri_2021/033.
Повний текст джерелаBrown, Nicholas, Hannah Macdonell, Emilie Stewart-Jones, and Stephan Gruber. Permafrost Data Systems: RCOP 2021 Data Workshop Report. NSERC/Carleton University, November 2021. http://dx.doi.org/10.22215/pn/10121001.
Повний текст джерелаVolkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.
Повний текст джерела