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Journal articles on the topic 'Objective data'

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Published: 29 June 2021
Last updated: 1 February 2022

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1

Stix, Gary. "Objective Data." Scientific American 266, no. 3 (March 1992): 108. http://dx.doi.org/10.1038/scientificamerican0392-108.

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2

Carr, Edward L. "Objective Data Analysis Conference." Bulletin of the American Meteorological Society 68, no. 5 (May 1, 1987): 481–85. http://dx.doi.org/10.1175/1520-0477-68.5.481.

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The Air Force Global Weather Central (AFGWC) hosted a conference on objective data analysis 2–4 October 1985. This was a continuation in a series of conferences on data analysis. Participants included both operational meteorologists and research meteorologists from various government's numerical weather prediction (NWP) facilities. The conference consisted of each participating facility reviewing their current status and future plans. Individual topics were then presented concerning data analysis, followed by small group discussions on meteorological and computational aspects of objective data analysis. Various conclusions were developed during the conference: 1) Optimal interpolation is the analysis method most commonly used at NWP facilities and is favored for its ability to assimilate data from many different observing platforms. 2) Promising work continues with the insertion of new data into the analysis model. 3) The fundamental difference between most analyses is the difference in the imposed quality control. 4) Further improvements in data assimilation are possible if archival procedures are performed to gather covariance statistics.
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3

Trapp, R. Jeffrey, and Charles A. Doswell. "Radar Data Objective Analysis." Journal of Atmospheric and Oceanic Technology 17, no. 2 (February 2000): 105–20. http://dx.doi.org/10.1175/1520-0426(2000)017<0105:rdoa>2.0.co;2.

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4

Gray, P. W., T. D. Mac Mahon, and M. U. Rajput. "Objective data evaluation procedures." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 286, no. 3 (January 1990): 569–75. http://dx.doi.org/10.1016/0168-9002(90)90918-v.

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5

K. Holland, Erin, and Major Major L. King. "Sleep Studies Need Objective Data." Journal of Psychosocial Nursing and Mental Health Services 46, no. 2 (February 1, 2008): 13–14. http://dx.doi.org/10.3928/02793695-20080201-07.

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6

Smith, G. D., and Y. Ben-Shlomo. "Objective data trials are needed." BMJ 312, no. 7044 (June 8, 1996): 1479–80. http://dx.doi.org/10.1136/bmj.312.7044.1479c.

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7

OBAYASHI, Shigeru. "Multi-Objective Optimization and Data Mining." Journal of the Society of Mechanical Engineers 109, no. 1050 (2006): 383–85. http://dx.doi.org/10.1299/jsmemag.109.1050_383.

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8

M’lan, Cyr Emile, and Ming-Hui Chen. "Objective Bayesian Inference for Bilateral Data." Bayesian Analysis 10, no. 1 (March 2015): 139–70. http://dx.doi.org/10.1214/14-ba890.

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9

Huber, Jessica E., Elaine Stathopoulos, Joan Sussman, and Kris Tjaden. "Obtaining Objective Data in Clinical Settings." ASHA Leader 15, no. 12 (October 2010): 12–15. http://dx.doi.org/10.1044/leader.ftr2.15122010.12.

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10

Noguchi, Kazutaka. "The objective lens for holographic data storage." Review of Laser Engineering 36, Supplement (2008): S27—S28. http://dx.doi.org/10.2184/lsj.36.s27.

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11

Rhee, Chanu, Sameer Kadri, Susan S. Huang, Michael V. Murphy, Lingling Li, Richard Platt, and Michael Klompas. "Objective Sepsis Surveillance Using Electronic Clinical Data." Infection Control & Hospital Epidemiology 37, no. 2 (November 3, 2015): 163–71. http://dx.doi.org/10.1017/ice.2015.264.

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OBJECTIVETo compare the accuracy of surveillance of severe sepsis using electronic health record clinical data vs claims and to compare incidence and mortality trends using both methods.DESIGNWe created an electronic health record–based surveillance definition for severe sepsis using clinical indicators of infection (blood culture and antibiotic orders) and concurrent organ dysfunction (vasopressors, mechanical ventilation, and/or abnormal laboratory values). We reviewed 1,000 randomly selected medical charts to characterize the definition’s accuracy and stability over time compared with a claims-based definition requiring infection and organ dysfunction codes. We compared incidence and mortality trends from 2003–2012 using both methods.SETTINGTwo US academic hospitals.PATIENTSAdult inpatients.RESULTSThe electronic health record–based clinical surveillance definition had stable and high sensitivity over time (77% in 2003–2009 vs 80% in 2012, P=.58) whereas the sensitivity of claims increased (52% in 2003–2009 vs 67% in 2012, P=.02). Positive predictive values for claims and clinical surveillance definitions were comparable (55% vs 53%, P=.65) and stable over time. From 2003 to 2012, severe sepsis incidence imputed from claims rose by 72% (95% CI, 57%–88%) and absolute mortality declined by 5.4% (95% CI, 4.6%–6.7%). In contrast, incidence using the clinical surveillance definition increased by 7.7% (95% CI, −1.1% to 17%) and mortality declined by 1.7% (95% CI, 1.1%–2.3%).CONCLUSIONSSepsis surveillance using clinical data is more sensitive and more stable over time compared with claims and can be done electronically. This may enable more reliable estimates of sepsis burden and trends.Infect. Control Hosp. Epidemiol. 2016;37(2):163–171
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12

Majcen, Mario, Paul Markowski, Yvette Richardson, David Dowell, and Joshua Wurman. "Multipass Objective Analyses of Doppler Radar Data." Journal of Atmospheric and Oceanic Technology 25, no. 10 (October 1, 2008): 1845–58. http://dx.doi.org/10.1175/2008jtecha1089.1.

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Abstract This note assesses the improvements in dual-Doppler wind syntheses by employing a multipass Barnes objective analysis in the interpolation of radial velocities to a Cartesian grid, as opposed to a more typical single-pass Barnes objective analysis. Steeper response functions can be obtained by multipass objective analyses; that is, multipass objective analyses are less damping at well-resolved wavelengths (e.g., 8–20Δ, where Δ is the data spacing) than single-pass objective analyses, while still suppressing small-scale (&lt;4Δ) noise. Synthetic dual-Doppler data were generated from a three-dimensional numerical simulation of a supercell thunderstorm in a way that emulates the data collection by two mobile radars. The synthetic radial velocity data from a pair of simulated radars were objectively analyzed to a grid, after which the three-dimensional wind field was retrieved by iteratively computing the horizontal divergence and integrating the anelastic mass continuity equation. Experiments with two passes and three passes of the Barnes filter were performed, in addition to a single-pass objective analysis. Comparison of the analyzed three-dimensional wind fields to the model wind fields suggests that multipass objective analysis of radial velocity data prior to dual-Doppler wind synthesis is probably worth the added computational cost. The improvements in the wind syntheses derived from multipass objective analyses are even more apparent for higher-order fields such as vorticity and divergence, and for trajectory calculations and pressure/buoyancy retrievals.
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13

Berger, James O., Victor De Oliveira, and Bruno Sansó. "Objective Bayesian Analysis of Spatially Correlated Data." Journal of the American Statistical Association 96, no. 456 (December 2001): 1361–74. http://dx.doi.org/10.1198/016214501753382282.

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14

McIntosh, Peter C. "Oceanographic data interpolation: Objective analysis and splines." Journal of Geophysical Research 95, no. C8 (1990): 13529. http://dx.doi.org/10.1029/jc095ic08p13529.

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15

KUDO, keiji. "Engineering Data Mining for Multi-Objective Problem." Reference Collection of Annual Meeting 2004.8 (2004): 205–6. http://dx.doi.org/10.1299/jsmemecjsm.2004.8.0_205.

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16

Altwaijry, Najwa, and Mohamed El Bachir Menai. "Data Structures in Multi-Objective Evolutionary Algorithms." Journal of Computer Science and Technology 27, no. 6 (November 2012): 1197–210. http://dx.doi.org/10.1007/s11390-012-1296-y.

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17

Sarycheva, Lyudmila V. "Objective Cluster Analysis of Data Based on GMDH." Journal of Automation and Information Sciences 40, no. 4 (2008): 28–48. http://dx.doi.org/10.1615/jautomatinfscien.v40.i4.30.

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18

V., Shyamala Susan, and Christopher T. "PRIVACY PRESERVING DATA MINING USING MULTIPLE OBJECTIVE OPTIMIZATION." ICTACT Journal on Soft Computing 07, no. 01 (October 1, 2016): 1366–71. http://dx.doi.org/10.21917/ijsc.2016.0189.

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19

Suresh, Kaushik, Debarati Kundu, Sayan Ghosh, Swagatam Das, and Ajith Abraham. "Data Clustering Using Multi-objective Differential Evolution Algorithms." Fundamenta Informaticae 97, no. 4 (2009): 381–403. http://dx.doi.org/10.3233/fi-2009-208.

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20

Aho, Ken. "MULTIVARIATE CLUSTERING FOR OBJECTIVE CLASSIFICATION OF VEGETATION DATA." Journal American Society of Mining and Reclamation 2006, no. 1 (June 30, 2006): 1–24. http://dx.doi.org/10.21000/jasmr06010001.

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21

Sei, Tomonari. "An objective general index for multivariate ordered data." Journal of Multivariate Analysis 147 (May 2016): 247–64. http://dx.doi.org/10.1016/j.jmva.2016.02.005.

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22

MacKay, David J. C. "Information-Based Objective Functions for Active Data Selection." Neural Computation 4, no. 4 (July 1992): 590–604. http://dx.doi.org/10.1162/neco.1992.4.4.590.

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Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.
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23

Levy, Gad, and Robert A. Brown. "A simple, objective analysis scheme for scatterometer data." Journal of Geophysical Research 91, no. C4 (1986): 5153. http://dx.doi.org/10.1029/jc091ic04p05153.

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24

Sasse, Edward A. "Objective evaluation of data in screening for disease." Clinica Chimica Acta 315, no. 1-2 (January 2002): 17–30. http://dx.doi.org/10.1016/s0009-8981(01)00710-0.

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25

Mitra, Sushmita, and Haider Banka. "Multi-objective evolutionary biclustering of gene expression data." Pattern Recognition 39, no. 12 (December 2006): 2464–77. http://dx.doi.org/10.1016/j.patcog.2006.03.003.

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26

Lobato, Fabio, Claudomiro Sales, Igor Araujo, Vincent Tadaiesky, Lilian Dias, Leonardo Ramos, and Adamo Santana. "Multi-objective genetic algorithm for missing data imputation." Pattern Recognition Letters 68 (December 2015): 126–31. http://dx.doi.org/10.1016/j.patrec.2015.08.023.

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27

Hanafi, Saïd, Gintaras Palubeckis, and Fred Glover. "Bi-objective optimization of biclustering with binary data." Information Sciences 538 (October 2020): 444–66. http://dx.doi.org/10.1016/j.ins.2020.05.078.

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28

Farias, M. C. Q., M. Carli, and S. K. Mitra. "Objective video quality metric based on data hiding." IEEE Transactions on Consumer Electronics 51, no. 3 (August 2005): 983–92. http://dx.doi.org/10.1109/tce.2005.1510512.

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29

Hegazy, Yasser A., and Paul W. Mayne. "Objective Site Characterization Using Clustering of Piezocone Data." Journal of Geotechnical and Geoenvironmental Engineering 128, no. 12 (December 2002): 986–96. http://dx.doi.org/10.1061/(asce)1090-0241(2002)128:12(986).

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30

Brunskog, Jonas, and Birgit Rasmussen. "Meta-analysis of subjective-objective sound insulation data." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3539. http://dx.doi.org/10.1121/1.4987480.

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31

LONG, NGUYEN CONG, NAWAPORN WISITPONGPHAN, PHAYUNG MEESAD, and HERWIG UNGER. "CLUSTERING STOCK DATA FOR MULTI-OBJECTIVE PORTFOLIO OPTIMIZATION." International Journal of Computational Intelligence and Applications 13, no. 02 (June 2014): 1450011. http://dx.doi.org/10.1142/s1469026814500114.

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Portfolio selection is a vital research field in modern finance. Multi-objective portfolio optimization problem is the portfolio selection process that results in the highest expected return rate and the lowest identified risk among the various financial assets. This paper proposes a model that can efficiently suggest a portfolio that is worth investing. First, a cluster analysis model is introduced in order to categorize a huge amount of stock data into several groups based on their associated return rate and the risk. Several validity indexes are used to select the optimal number of clusters/stocks to be included in the portfolio. Finally, the multi-objective genetic algorithm is used to build portfolio optimization with highest return rate and lowest risk. The proposed model is tested on the data obtained from the Stock Exchange of Thailand.
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32

Palmieri, Francesco A. N., and Domenico Ciuonzo. "Objective priors from maximum entropy in data classification." Information Fusion 14, no. 2 (April 2013): 186–98. http://dx.doi.org/10.1016/j.inffus.2012.01.012.

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33

Antolin, Albert A., Joseph E. Tym, Angeliki Komianou, Ian Collins, Paul Workman, and Bissan Al-Lazikani. "Objective, Quantitative, Data-Driven Assessment of Chemical Probes." Cell Chemical Biology 25, no. 2 (February 2018): 194–205. http://dx.doi.org/10.1016/j.chembiol.2017.11.004.

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34

Korhonen, Pekka, and Jyrki Wallenius. "Using qualitative data in multiple objective linear programming." European Journal of Operational Research 48, no. 1 (September 1990): 81–87. http://dx.doi.org/10.1016/0377-2217(90)90064-i.

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35

Jafarian-Moghaddam, Ahmad Reza, and Keivan Ghoseiri. "Fuzzy dynamic multi-objective Data Envelopment Analysis model." Expert Systems with Applications 38, no. 1 (January 2011): 850–55. http://dx.doi.org/10.1016/j.eswa.2010.07.045.

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36

Kazianka, Hannes. "Objective Bayesian Analysis of Geometrically Anisotropic Spatial Data." Journal of Agricultural, Biological, and Environmental Statistics 18, no. 4 (April 23, 2013): 514–37. http://dx.doi.org/10.1007/s13253-013-0137-y.

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37

He, Jun, and Mark Bathe. "Objective, Bayesian Analysis of Fluorescence Correlation Spectroscopy Data." Biophysical Journal 100, no. 3 (February 2011): 175a. http://dx.doi.org/10.1016/j.bpj.2010.12.1177.

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38

Schnaidt, Sebastian, Dennis Conway, Lars Krieger, and Graham Heinson. "Pareto-Optimal Multi-objective Inversion of Geophysical Data." Pure and Applied Geophysics 175, no. 6 (January 29, 2018): 2221–36. http://dx.doi.org/10.1007/s00024-018-1784-2.

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39

Sanford, Richard F., Charles T. Pierson, and Robert A. Crovelli. "An objective replacement method for censored geochemical data." Mathematical Geology 25, no. 1 (January 1993): 59–80. http://dx.doi.org/10.1007/bf00890676.

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40

Ho, Trong-Viet, Yves Deville, and Olivier Bonaventure. "Multi-objective traffic engineering for data center networks." Computer Networks 65 (June 2014): 167–82. http://dx.doi.org/10.1016/j.comnet.2014.03.018.

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41

Thiele, Maik, Andreas Bader, and Wolfgang Lehner. "Multi-objective scheduling for real-time data warehouses." Computer Science - Research and Development 24, no. 3 (April 2, 2009): 137–51. http://dx.doi.org/10.1007/s00450-009-0062-z.

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42

Wong, Kelvin Kian Loong, Zhihua Liu, and Quan Zou. "Multi-objective optimization and data analysis in informationization." Computing 101, no. 6 (April 19, 2019): 495–98. http://dx.doi.org/10.1007/s00607-019-00718-3.

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43

Moreno, E., F. J. Vázquez-Polo, and M. A. Negrín. "Objective Bayesian meta-analysis for sparse discrete data." Statistics in Medicine 33, no. 21 (April 8, 2014): 3676–92. http://dx.doi.org/10.1002/sim.6163.

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44

Savic, D. A., O. Giustolisi, and D. Laucelli. "Asset deterioration analysis using multi-utility data and multi-objective data mining." Journal of Hydroinformatics 11, no. 3-4 (July 1, 2009): 211–24. http://dx.doi.org/10.2166/hydro.2009.019.

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Physically-based models derive from first principles (e.g. physical laws) and rely on known variables and parameters. Because these have physical meaning, they also explain the underlying relationships of the system and are usually transportable from one system to another as a structural entity. They only require model parameters to be updated. Data-driven or regressive techniques involve data mining for modelling and one of the major drawbacks of this is that the functional form describing relationships between variables and the numerical parameters is not transportable to other physical systems as is the case with their classical physically-based counterparts. Aimed at striking a balance, Evolutionary Polynomial Regression (EPR) offers a way to model multi-utility data of asset deterioration in order to render model structures transportable across physical systems. EPR is a recently developed hybrid regression method providing symbolic expressions for models and works with formulae based on pseudo-polynomial expressions, usually in a multi-objective scenario where the best Pareto optimal models (parsimony versus accuracy) are selected from data in a single case study. This article discusses the improvement of EPR in dealing with multi-utility data (multi-case study) where it has been tried to achieve a general model structure for asset deterioration prediction across different water systems.
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45

Mokadem, Riad, and Abdelkader Hameurlain. "Data replication strategies with performance objective in data grid systems: a survey." International Journal of Grid and Utility Computing 6, no. 1 (2015): 30. http://dx.doi.org/10.1504/ijguc.2015.066395.

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46

Eischeid, Jon K., C. Bruce Baker, Thomas R. Karl, and Henry F. Diaz. "The Quality Control of Long-Term Climatological Data Using Objective Data Analysis." Journal of Applied Meteorology 34, no. 12 (December 1995): 2787–95. http://dx.doi.org/10.1175/1520-0450(1995)034<2787:tqcolt>2.0.co;2.

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47

Liu, Qi, Jiahao Liu, and Dunhu Liu. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives." Sustainability 10, no. 10 (October 18, 2018): 3760. http://dx.doi.org/10.3390/su10103760.

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This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an improved multi-objective particle swarm optimization (MOPSO) process and an entropy weight method-based evaluation process. The MOPSO process is utilized to obtain a set of Pareto optimal solutions, and the entropy weight method-based evaluation process is utilized to select the final solution from Pareto optimal solutions. Numerical experiments are conducted based on large-scale GPS data. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated. Moreover, the comparison of single-objective and multi-objective models validates the efficiency and necessity of the proposed multi-objective model in public charging station location problems.
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48

Deely, John, Stephen Hynes, and John Curtis. "Are objective data an appropriate replacement for subjective data in site choice analysis?" Journal of Environmental Economics and Policy 8, no. 2 (October 7, 2018): 159–78. http://dx.doi.org/10.1080/21606544.2018.1528895.

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49

Fraser, C. G., Heather P. Stevenson, and Ian M. G. Kennedy. "Biological variation data are necessary prerequisites for objective autoverification of clinical laboratory data." Accreditation and Quality Assurance 7, no. 11 (November 1, 2002): 455–60. http://dx.doi.org/10.1007/s00769-002-0526-3.

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50

Roberts, David L., Alper Bozkurt, Timothy Holder, Evan Williams, Sean Mealin, Marc Foster, and Zachary Cleghern. "A cloud data collection platform for canine behavioural prediction using objective sensor data." International Journal of Cloud Computing 10, no. 3 (2021): 247. http://dx.doi.org/10.1504/ijcc.2021.10041443.

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