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Zeitschriftenartikel zum Thema „Objective data“

Autor: Grafiati

Veröffentlicht am 29. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

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

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2

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

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3

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 (1990): 569–75. http://dx.doi.org/10.1016/0168-9002(90)90918-v.

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4

Carr, Edward L. "Objective Data Analysis Conference." Bulletin of the American Meteorological Society 68, no. 5 (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

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A, Bharathi, Siva C, Suguna Angamuthu, and Thangamani M. "Multi Objective Concerned Network Coding Technique for Optimal Data Transmission." Webology 18, no. 05 (2021): 1191–203. http://dx.doi.org/10.14704/web/v18si05/web18300.

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The Internet of Things (IoT) seems to developed in the real-world scenario due to increased utilization of sensor driven technologies. There are various research works has been proposed earlier for the network coding to ensure the reliable data transmission. However, existing research techniques doesn’t focus on the reliable route path selection which might affect the rate of data transmission. These issues are focused in the proposed research method by introducing the method namely Multi-Objective concerned Network Coding Technique (MO-NCT). In this work initially multicast tree construction

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

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7

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

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8

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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9

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

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10

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

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11

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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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 (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 fro

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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 (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

Rhee, Chanu, Sameer Kadri, Susan S. Huang, et al. "Objective Sepsis Surveillance Using Electronic Clinical Data." Infection Control & Hospital Epidemiology 37, no. 2 (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 cla

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17

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

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18

Avdagić-Golub, Elma, Nermin Goran, Bakir Karahodža, Muhamed Begović, and Belma Memić. "Call rating based on objective data extraction." Telfor Journal 15, no. 2 (2023): 56–62. http://dx.doi.org/10.5937/telfor2302056a.

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Interaction channels are special opportunities to improve customer satisfaction by offering a consistent problem-solving experience. Contact center employees are the link between the company and the customer. They are responsible for maintaining an appropriate relationship between the company and the customer. So, they are personally responsible for the customer experience. In this paper, we present an objective evaluation method for evaluating customer-agent interaction, i.e., evaluating the effectiveness of the realization of customer requests from calls. The evaluation method is automatic a

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Vanessa, Kathenin do Nascimento Felix Silva. "CIVIL LIABILITY OF THE DATA CONTROLLER FOR THE LEAKAGE OF EMPLOYEE DATA." International Journal of Education and Research 1, no. 1 (2025): 28–36. https://doi.org/10.5281/zenodo.15131160.

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This article deals with the civil liability of data controllers due to the leakage of employee data. This study had as its research question: what is the type of civil liability of the data controller due to the leakage of employee data and its consequences? Given this problem, the following general objective emerged: to identify the type of civil liability applied to controllers due to the leakage of employee data and its application consequences. The specific objectives are: to study the General Data Protection Law, in its general aspects; to understand the flow of data in the labor contract

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Aakriti, Shukla, and Prasad Tiwari Damodar. "Multi-Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 1, no. 5 (2021): 1–4. https://doi.org/10.54105/ijainn.E1040.121521.

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Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis

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21

V, Sridhar Reddy, Jayanthi N, Sharon Rose Victor Juvvanapudi, Srinivas Bachu, and Sumalatha Madipalli. "Multi Objective Data Transformation in Hybrid Clouds Networks for Offloading Data." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3691–700. http://dx.doi.org/10.12694/scpe.v25i5.3099.

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Recently hybrid cloud solutions integrating public and private cloud is proposed to address the privacy and security concerns faced by Enterprises in their data offloading decisions. In these solutions, the transformed data is kept in public cloud while transformation keys are kept in private cloud. The existing works for data transformation used in hybrid clouds does not address multiple objectives of privacy, security, fine grained access control, utility preservation for mining and data retrieval efficiency. This work proposes a multi objective data transformation technique for hybrid cloud

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22

Liu, Qi, Jiahao Liu, and Dunhu Liu. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives." Sustainability 10, no. 10 (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

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23

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

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24

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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25

Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using MOGA." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–28. http://dx.doi.org/10.4018/ijamc.292499.

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The COVID 19 Pandemic, has resulted in large scale of generation of Big data. This Big data is heterogeneous and includes the data of people infected with corona virus, the people who were in contact of infected person, demographics of infected person, data on corona testing, huge amount of GPS data of people location, and large number of unstructured data about prevention and treatment of COVID 19. Thus, the pandemic has resulted in producing several Zeta bytes of structured, semi-structured and unstructured data. The challenge is to process this Big data, which has the characteristics of ver

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26

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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27

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

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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 (2005): 983–92. http://dx.doi.org/10.1109/tce.2005.1510512.

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29

King, Major L. "Response: Sleep Studies Need Objective Data." Journal of Psychosocial Nursing and Mental Health Services 46, no. 2 (2008): 14. https://doi.org/10.3928/02793695-20080201-08a.

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30

King, Major L. "Response: Sleep Studies Need Objective Data." Journal of Psychosocial Nursing and Mental Health Services 46, no. 2 (2008): 14. https://doi.org/10.3928/02793695-20080201-28.

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31

MacKay, David J. C. "Information-Based Objective Functions for Active Data Selection." Neural Computation 4, no. 4 (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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32

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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33

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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34

Lobato, Fabio, Claudomiro Sales, Igor Araujo, et al. "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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35

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

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36

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

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37

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

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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 (2018): 2221–36. http://dx.doi.org/10.1007/s00024-018-1784-2.

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39

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

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40

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

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41

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

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42

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

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43

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

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44

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 (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

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45

Encinas-Bartos, Alex P., Nikolas O. Aksamit, and George Haller. "Quasi-objective eddy visualization from sparse drifter data." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 11 (2022): 113143. http://dx.doi.org/10.1063/5.0099859.

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We employ a recently developed single-trajectory Lagrangian diagnostic tool, the trajectory rotation average [Formula: see text], to visualize oceanic vortices (or eddies) from sparse drifter data. We apply the [Formula: see text] to two drifter data sets that cover various oceanographic scales: the Grand Lagrangian Deployment and the Global Drifter Program. Based on the [Formula: see text], we develop a general algorithm that extracts approximate eddy boundaries. We find that the [Formula: see text] outperforms other available single-trajectory-based eddy detection methodologies on sparse dri

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46

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

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47

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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48

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

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49

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

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50

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

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