Relevant bibliographies by topics / Data Importance

Academic literature on the topic 'Data Importance'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data Importance"

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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.

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Brown, Renée F. "The Importance of Data Citation." BioScience 71, no. 3 (March 1, 2021): 211. http://dx.doi.org/10.1093/biosci/biab012.

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Spetz, 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.

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In 1977, the federal government launched the nation's largest and most significant program to collect data on the registered nurse (RN) workforce of the United States—the National Sample Survey of Registered Nurses (NSSRN). This survey is conducted by the U.S. Health Resources and Services Administration, first in 1977 and then every 4 years since 1980. This article offers the history of the NSSRN and a review of the ways in which the NSSRN data have been used to examine education, demographics, employment, shortages, and other aspects of the RN workforce. The influence this body of research has had on policymaking is explored. Recommendations for future research are offered, in the hope that future waves of the NSSRN will continue to be used to their fullest potential.
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Nickels, 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.

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Wold, 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.

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Samuelson, 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.

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Jang, 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.

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Richardson, 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.

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Chaoli 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.

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Jollis, 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.

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Dissertations / Theses on the topic "Data Importance"

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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.

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Designing on-premise hardware platforms to deal with big data analytics should be done in a way in which the available resources can be scaled both up and down depending on future needs. Two of the main components of an analytics cluster is the data storage and computational part. Separating those two components yields great value but can come with the price of performance loss if not set up properly. The objective of this thesis is to examine how much the performance gets impacted when the computational and storage part gets divided into different hardware nodes. To get data on how well this separation could be done, several tests were conducted on different hardware setups. These tests included real-world workloads run on configurations where both the storage and the computation took place on the same nodes and on configurations where these components were separated. While those tests were done on a smaller scale with only three compute nodes parallel, tests with similar workloads were also conducted on a larger scale with up to 32 computational nodes. The tests revealed that separating compute from storage on a smaller scale could be done without any significant performance drawbacks. However,when the computational components grew large enough,bottlenecks in the storage cluster surfaced. While the results on a smaller scale were satisfactory,further improvements could be made for the larger-scale tests.
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Stephens, Joshua J. "Data Governance Importance and Effectiveness| Health System Employee Perception." Thesis, Central Michigan University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751061.

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The 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.

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Bordoloi, Udeepta Dutta. "Importance-driven algorithms for scientific visualization." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1118952958.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title 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
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Northrop, Amanda Rosalind. "Importance of various data sources in deterministic stock assessment models." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1002811.

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In fisheries, advice for the management of fish populations is based upon management quantities that are estimated by stock assessment models. Fisheries stock assessment is a process in which data collected from a fish population are used to generate a model which enables the effects of fishing on a stock to be quantified. This study determined the effects of various data sources, assumptions, error scenarios and sample sizes on the accuracy with which the age-structured production model and the Schaefer model (assessment models) were able to estimate key management quantities for a fish resource similar to the Cape hakes (Merluccius capensis and M. paradoxus). An age-structured production model was used as the operating model to simulate hypothetical fish resource population dynamics for which management quantities could be determined by the assessment models. Different stocks were simulated with various harvest rate histories. These harvest rates produced Downhill trip data, where harvest rates increase over time until the resource is close to collapse, and Good contrast data, where the harvest rate increases over time until the resource is at less than half of it’s exploitable biomass, and then it decreases allowing the resource to rebuild. The accuracy of the assessment models were determined when data were drawn from the operating model with various combinations of error. The age-structured production model was more accurate at estimating maximum sustainable yield, maximum sustainable yield level and the maximum sustainable yield ratio. The Schaefer model gave more accurate estimates of Depletion and Total Allowable Catch. While the assessment models were able to estimate management quantities using Downhill trip data, the estimates improved significantly when the models were tuned with Good contrast data. When autocorrelation in the spawner-recruit curve was not accounted for by the deterministic assessment model, inaccuracy in parameter estimates were high. The assessment model management quantities were not greatly affected by multinomial ageing error in the catch-at-age matrices at a sample size of 5000 otoliths. Assessment model estimates were closer to their true values when log-normal error were assumed in the catch-at-age matrix, even when the true underlying error were multinomial. However, the multinomial had smaller coefficients of variation at all sample sizes, between 1000 and 10000, of otoliths aged. It was recommended that the assessment model is chosen based on the management quantity of interest. When the underlying error is multinomial, the weighted log-normal likelihood function should be used in the catch-at-age matrix to obtain accurate parameter estimates. However, the multinomial likelihood should be used to minimise the coefficient of variation. Investigation into correcting for autocorrelation in the stock-recruitment relationship should be carried out, as it had a large effect on the accuracy of management quantities.
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Wan, 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.

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Matthäus, 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.

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Short-term mine plans are the key operational basis for ore production targets ranging from shift to weekly or monthly targets. Short-term plans cover detailed operational subprocesses such as development, extraction and backfill schedules as well as materials handling and blending processes. The aim is to make long-term goals feasible by providing a constant plant feed that complies with quality constraints. Short-term mine planning highly depends on the accuracy of the resource model as well as the current production status and equipment fleet. Most of these parameters are characterized by uncertainties due to a lack of information and equipment reliability. At the same time, concentrate production and quality must be kept within acceptable ranges to ensure productivity and economic viability of the operation. Within the EU-funded Real-Time Mining project, the reduction of uncertainty in mine planning is carried by using real-time data. Ore and rock characteristics of active faces and equipment data are iteratively integrated in a simulation-based optimization tool. Therefore, predicted processing plant efficiencies can be met by delivering constant ore grades. Hence, a constant concentrate quality is ensured and long-term targets can be fulfilled. Consequently, a more reliable exploitation plan of the mineral reserve is facilitated.
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Mafu, 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.

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Thesis (MCom)--Stellenbosch University, 2014.
ENGLISH 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.
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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.

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Short-term mine plans are the key operational basis for ore production targets ranging from shift to weekly or monthly targets. Short-term plans cover detailed operational subprocesses such as development, extraction and backfill schedules as well as materials handling and blending processes. The aim is to make long-term goals feasible by providing a constant plant feed that complies with quality constraints. Short-term mine planning highly depends on the accuracy of the resource model as well as the current production status and equipment fleet. Most of these parameters are characterized by uncertainties due to a lack of information and equipment reliability. At the same time, concentrate production and quality must be kept within acceptable ranges to ensure productivity and economic viability of the operation. Within the EU-funded Real-Time Mining project, the reduction of uncertainty in mine planning is carried by using real-time data. Ore and rock characteristics of active faces and equipment data are iteratively integrated in a simulation-based optimization tool. Therefore, predicted processing plant efficiencies can be met by delivering constant ore grades. Hence, a constant concentrate quality is ensured and long-term targets can be fulfilled. Consequently, a more reliable exploitation plan of the mineral reserve is facilitated.
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Kaponen, 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.

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Nalini, 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.

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Interruptions in power supply are inevitable due to faults in power system distribution network. These interruptions are not only expensive for the customers but also for the distribution system operator in the form of penalties. Increase in system redundancy or the use of component-specific sensors can help in reduction of interruptions. However, these options are not always economically feasible. Therefore, there is a need to check for other possibilities to reduce the risk of outages. The data stored in substations can be used for reducing the risk of outages by deriving component importance indices followed by ranking and predicting the outages. This thesis presents component importance indices derived by identifying the critical components in the grid and assigning index based on certain criterion. The model for predicting the faults is based on the weather conditions observed during the outages in the past. Component importance indices are derived and ranked based on the de-energisation time of components, frequency and impact of outages. This helps prioritize components according to the chosen criterion and adapt monitoring strategies by focusing on the most critical components. Based on categorical Naive Bayes, a model is developed to predict the probability of fault/failure, location and component type likely to be affected for a given set of weather conditions. The results from the component importance indices reveal that each component’s rank varies based on the chosen criterion. This indicates that certain components are critical with respect to specific criterion and not all criteria. However, some components are ranked high in all the methods. These components are critical and need focused monitoring. The reliability of results from component importance indices to a great extent depends on the time frame of the outage data considered for analysis. The prediction model can alert the distribution system operator regarding the possible outages in the network for a given set of weather conditions. However, the prediction of location and component type likely to be affected is relatively inaccurate, since the number of outages considered in the time frame is low. By updating the model regularly with new data, the predictions would be more accurate.
Avbrott 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.
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Books on the topic "Data Importance"

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Euclid, ed. Dedomena: Euclid's Data, or, The importance of being given. Copenhagen: Museum Tusculanum Press, 2003.

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Comin, Diego. Using investment data to assess the importance of price mismeasurement. Cambridge, MA: National Bureau of Economic Research, 2004.

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Goldstein, 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.

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Patterson, K. D. Modelling the Livingston price expectations data: The importance of monthly information. Reading: University of Reading Department of Economics, 1985.

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Maclean, Alex. Analysing the importance of wireless technology andinvestigating data transmission over wireless networks. Oxford: Oxford Brookes University, 2001.

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Moshirpour, 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.

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Mairesse, Jacques. The importance of R & D for innovation: A reassesment using French survey data. Cambridge, MA: National Bureau of Economic Research, 2004.

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McCardell, Orla. The importance of building maintenance and the availablity of building maintenance cost data. [s.l: The Author], 1997.

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Mairesse, Jacques. The importance of R & D for innovation: A reassessment using French survey data. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Slawson, Nadine. The value and importance of differentiation when teaching handling data to junior age children. Cardiff: CIHE, 1995.

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Book chapters on the topic "Data Importance"

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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.

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Ruggles, 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.

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Alford, 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.

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Link, 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.

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Zhao, 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.

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Zhang, 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.

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Devine, 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.

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Jugulum, 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.

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Fukuda, 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.

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He, 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.

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Conference papers on the topic "Data Importance"

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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.

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Ashby, 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.

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Faisal, 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.

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Waldschütz, 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.

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Haque, 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.

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Anagnostopoulos, 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.

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Proko, 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.

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Proko, 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.

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Tekieh, 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.

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Walsh, 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.

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Reports on the topic "Data Importance"

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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.

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School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geospatial aspects of these transitions. Few LMIS systems fully consider or integrate geospatial school-to-work transition information, ignoring data critical to understanding and supporting successful and sustainable employment: employer locations; transportation infrastructure; commute time, distance, and cost; location of employment services; and other geographic barriers to employment. We provide recently collected geospatial school-to-work transition data from South Africa and Kenya to demonstrate the importance of these data and their implications for labor market and urban development policy.
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Comin, 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.

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Biersdorf, 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.

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Mairesse, 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.

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Matthew 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.

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Blau, 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.

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Glaser, 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.

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Nchare, 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.

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This note examines the effects of School-Based Management Committees (SBMC) on the quality of education in Nigeria based on existing studies and completed programmes. We will investigate whether actions implemented by SBMCs improve learning outcomes and teachers’ performance. In the first section, we will discuss the findings from data collected by the Education Sector Programme in Nigeria (ESSPIN). In the second section, we will discuss findings from an exploratory analysis using survey data collected for the Service Delivery Education Indicators (SDI) in Nigeria. In the conclusion, we will discuss some lessons learned and the implications for the RISE Nigeria SBMC research design.
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Brown, 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.

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NSERC PermafrostNet hosted a data systems workshop at the 2021 Regional Conference on Permafrost, held online in October 2021. The workshop featured invited speakers Ashley Rudy from the Northwest Territories Geological Survey and Jeanette Nötzli from the Swiss Permafrost Monitoring Network (PERMOS). Attendees participated in breakout rooms and plenary discussion to identify current problems and limitations with permafrost data systems and to recommend how efforts can be better connected or coordinated. The final report summarizes the conclusions and provides a record of the interactions and discussions that occurred. The workshop follows the 2020 Permafrost Data Workshop, which highlighted the importance of a community of practice and ongoing communication to improve the interoperability of permafrost data. In addition to the concrete objectives of identifying challenges and recommendations, the 2021 workshop was a way for members of the permafrost community to share ideas, and to cross-pollinate knowledge between sectors and disciplines of permafrost science.
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Volkova, 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.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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