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Thematische Bibliographien / Objective data

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Auswahl der wissenschaftlichen Literatur zum Thema „Objective data“

Autor: Grafiati

Veröffentlicht am 29. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Objective data"

1

Stix, Gary. „Objective Data“. Scientific American 266, Nr. 3 (März 1992): 108. http://dx.doi.org/10.1038/scientificamerican0392-108.

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Carr, Edward L. „Objective Data Analysis Conference“. Bulletin of the American Meteorological Society 68, Nr. 5 (01.05.1987): 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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Trapp, R. Jeffrey, und Charles A. Doswell. „Radar Data Objective Analysis“. Journal of Atmospheric and Oceanic Technology 17, Nr. 2 (Februar 2000): 105–20. http://dx.doi.org/10.1175/1520-0426(2000)017<0105:rdoa>2.0.co;2.

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Gray, P. W., T. D. Mac Mahon und M. U. Rajput. „Objective data evaluation procedures“. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 286, Nr. 3 (Januar 1990): 569–75. http://dx.doi.org/10.1016/0168-9002(90)90918-v.

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K. Holland, Erin, und Major Major L. King. „Sleep Studies Need Objective Data“. Journal of Psychosocial Nursing and Mental Health Services 46, Nr. 2 (01.02.2008): 13–14. http://dx.doi.org/10.3928/02793695-20080201-07.

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Smith, G. D., und Y. Ben-Shlomo. „Objective data trials are needed“. BMJ 312, Nr. 7044 (08.06.1996): 1479–80. http://dx.doi.org/10.1136/bmj.312.7044.1479c.

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OBAYASHI, Shigeru. „Multi-Objective Optimization and Data Mining“. Journal of the Society of Mechanical Engineers 109, Nr. 1050 (2006): 383–85. http://dx.doi.org/10.1299/jsmemag.109.1050_383.

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M’lan, Cyr Emile, und Ming-Hui Chen. „Objective Bayesian Inference for Bilateral Data“. Bayesian Analysis 10, Nr. 1 (März 2015): 139–70. http://dx.doi.org/10.1214/14-ba890.

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Huber, Jessica E., Elaine Stathopoulos, Joan Sussman und Kris Tjaden. „Obtaining Objective Data in Clinical Settings“. ASHA Leader 15, Nr. 12 (Oktober 2010): 12–15. http://dx.doi.org/10.1044/leader.ftr2.15122010.12.

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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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Dissertationen zum Thema "Objective data"

1

Kwoh, Chee Keong. „Probabilistic reasoning from correlated objective data“. Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307686.

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Kirkland, Oliver. „Multi-objective evolutionary algorithms for data clustering“. Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/51331/.

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In this work we investigate the use of Multi-Objective metaheuristics for the data-mining task of clustering. We �first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform investigations into the performance of different Multi-Objective clustering algorithms. In the context of clustering, a robust measure for evaluating clustering solutions is an important component of an algorithm. These Cluster Quality Measures (CQMs) should rely solely on the structure of the clustering solution. A robust CQM should have three properties: it should be able to reward a \good" clustering solution; it should decrease in value monotonically as the solution quality deteriorates and, it should be able to evaluate clustering solutions with varying numbers of clusters. We review existing CQMs and present an experimental evaluation of their robustness. We find that measures based on connectivity are more robust than other measures for cluster evaluation. We then introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Objective optimisation in clustering is desirable because it permits the incorporation of multiple measures of cluster quality. Since the definition of what constitutes a good clustering is far from clear, it is beneficial to develop algorithms that allow for multiple CQMs to be accommodated. The selection of the clustering quality measures to use as objectives for MOCA is informed by our previous work with internal evaluation measures. We explain the implementation details and perform experimental work to establish its worth. We compare MOCA with k-means and find some promising results. We�find that MOCA can generate a pool of clustering solutions that is more likely to contain the optimal clustering solution than the pool of solutions generated by k-means. We also perform an investigation into the performance of different implementations of MOEA algorithms for clustering. We�find that representations of clustering based around centroids and medoids produce more desirable clustering solutions and Pareto fronts. We also �find that mutation operators that greatly disrupt the clustering solutions lead to better exploration of the Pareto front whereas mutation operators that modify the clustering solutions in a more moderate way lead to higher quality clustering solutions. We then perform more specific investigations into the performance of mutation operators focussing on operators that promote clustering solution quality, exploration of the Pareto front and a hybrid combination. We use a number of techniques to assess the performance of the mutation operators as the algorithms execute. We confirm that a disruptive mutation operator leads to better exploration of the Pareto front and mutation operators that modify the clustering solutions lead to the discovery of higher quality clustering solutions. We find that our implementation of a hybrid mutation operator does not lead to a good improvement with respect to the other mutation operators but does show promise for future work.

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Fieldsend, Jonathan E. „Novel algorithms for multi-objective search and their application in multi-objective evolutionary neural network training“. Thesis, University of Exeter, 2003. http://hdl.handle.net/10871/11706.

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Brown, Nathan C. (Nathan Collin). „Early building design using multi-objective data approaches“. Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123573.

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Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-219).
During the design process in architecture, building performance and human experience are increasingly understood through computation. Within this context, this dissertation considers how data science and interactive optimization techniques can be combined to make simulation a more effective component of a natural early design process. It focuses on conceptual design, since technical principles should be considered when global decisions are made concerning the massing, structural system, and other design aspects that affect performance. In this early stage, designers might simulate structure, energy, daylighting, thermal comfort, acoustics, cost, and other quantifiable objectives. While parametric simulations offer the possibility of using a design space exploration framework to make decisions, their resulting feedback must be synthesized together, along with non-quantifiable design goals.
Previous research has developed optimization strategies to handle such multi-objective scenarios, but opportunities remain to further adapt optimization for the creative task of early building design, including increasing its interactivity, flexibility, accessibility, and ability to both support divergent brainstorming and enable focused performance improvement. In response, this dissertation proposes new approaches to parametric design space formulation, interactive optimization, and diversity-based design. These methods span in utility from early ideation, through global design exploration, to local exploration and optimization. The first presented technique uses data science methods to interrogate, transform, and, for specific cases, generate design variables for exploration. The second strategy involves interactive stepping through a design space using estimated gradient information, which offers designers more freedom compared to automated solvers during local exploration.
The third method addresses computational measurement of diversity within parametric design and demonstrates how such measurements can be integrated into creative design processes. These contributions are demonstrated on an integrated early design example and preliminarily validated using a design study that provides feedback on the habits and preferences of architects and engineers while engaging with data-driven tools. This study reveals that performance-enabled environments tend to improve simulated design objectives, while designers prefer more flexibility than traditional automated optimization approaches when given the choice. Together, these findings can stimulate further development in the integration of interactive approaches to multi-objective early building design. Key words: design space exploration, conceptual design, design tradeoffs, interactive design tools, structural design, sustainable design, multi-objective optimization, data science, surrogate modeling
by Nathan C. Brown.
Ph. D. in Architecture: Building Technology
Ph.D.inArchitecture:BuildingTechnology Massachusetts Institute of Technology, Department of Architecture

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Mostaghim, Sanaz. „Multi-objective evolutionary algorithms data structures, convergence, and diversity /“. [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974405604.

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Furst, Séverine. „Multi-objective optimization for joint inversion of geodetic data“. Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS017/document.

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La surface terrestre est affectée par de nombreux processus locaux tels que des événements volcaniques, des glissements de terrain ou des tremblements de terre. Parallèlement à ces processus naturels, les activités anthropiques, y compris l’extraction et le stockage des ressources profondes (par exemple, les minéraux ou les hydrocarbures) façonnent la Terre à différentes échelles spatiales et temporelles. Ces mécanismes produisent une déformation du sol qui peut être détectée par divers instruments et techniques géodésiques tel que le GNSS, l’InSAR, les inclinomètres. Le but de cette thèse est de développer un outil numérique permettant l’inversion conjointe de multiples données géodésiques associées à la déformation de la plaque ou au changement de contrainte volumique en profondeur. Quatre types d’applications sont ciblés: la déformation intersismiques des plaques, la déformation des volcans, l’exploitation minière profonde et l’extraction de pétrole et de gaz. Différentes complexités du modèle inverse ont été considérées: le niveau I considère un seul type de données géodésiques avec un processus indépendant du temps. Une application est réalisée avec l’inversion des données GPS à travers le sud de la Californie pour déterminer les variations latérales de la rigidité lithosphérique (Furst et al., 2017). Le niveau II représente également un seul type de données géodésiques mais avec un processus dépendant du temps. La détermination conjointe de l’historique des changements de contrainte et des paramètres de dérive d’un réseau d’inclinomètres est étudiée à l’aide d’un exemple synthétique (Furst et al., soumis). Le niveau III considère différents types de données géodésiques et un processus dépendant du temps. Un réseau fictif combinant des données GNSS, InSAR, inclinométriques et de nivellement est défini pour calculer le changement de volume dépendant du temps d’une source profonde de déformation. Une méthodologie pour implémenter ces différents niveaux de complexité est développée dans un seul logiciel. Parce que le problème inverse peut être mal posé, la minimisation de la fonctionnelle peut produire plusieurs minima. Par conséquent, un algorithme d’optimisation global est utilisé (Mohammadi and Saïac, 2003). Le problème direct est traité en utilisant un ensemble de modèles élastiques numériques et analytiques permettant de modéliser les processus de déformation en profondeur. Grâce à ces développements numériques, des avancées concernant les problèmes inverses en géodésie devraient être possibles telle que l’inversion jointe de différents types de données géodésiques acquises lors de la surveillance des volcans. Dans cette perspective, la possibilité de déterminer par inversion les paramètres de dérive des inclinomètres permettrait une détermination précise des sources de déformation profondes. En outre, la méthodologie développée peut être utilisée pour une surveillance précise de la déformation des réservoirs de pétrole et de gaz
The Earth’s surface is affected by numerous local processes like volcanic events, landslides or earthquakes. Along with these natural processes, anthropogenic activities including extraction and storage of deep resources (e.g. minerals, hydrocarbons) shape the Earth at different space and time scales. These mechanisms produce ground deformation that can be detected by various geodetic instruments like GNSS, InSAR, tiltmeters, for example. The purpose of the thesis is to develop a numerical tool to provide the joint inversion of multiple geodetic data associated to plate deformation or volume strain change at depth. Four kinds of applications are targeted: interseismic plate deformation, volcano deformation, deep mining, and oil & gas extraction. Different inverse model complexities were considered: the I-level considers a single type of geodetic data with a time independent process. An application is made with inverting GPS data across southern California to determine the lateral variations of lithospheric rigidity (Furst et al., 2017). The II-level also accounts for a single type of geodetic data but with a time-dependent process. The joint determination of strain change history and the drift parameters of a tiltmeter network is studied through a synthetic example (Furst et al., submitted). The III-level considers different types of geodetic data and a timedependent process. A fictitious network made by GNSS, InSAR, tiltmeters and levelling surveys is defined to compute the time dependent volume change of a deep source of strain. We develop a methodology to implement these different levels of complexity in a single software. Because the inverse problem is possibly ill-posed, the functional to minimize may display several minima. Therefore, a global optimization algorithm is used (Mohammadi and Saïac, 2003). The forward part of the problem is treated by using a collection of numerical and analytical elastic models allowing to model the deformation processes at depth. Thanks to these numerical developments, new advances for inverse geodetic problems should be possible like the joint inversion of various types of geodetic data acquired for volcano monitoring. In this perspective, the possibility to determine by inverse problem the tiltmeter drift parameters should allow for a precise determination of deep strain sources. Also, the developed methodology can be used for an accurate monitoring of oil & gas reservoir deformation

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Ray, Subhasis. „Multi-objective optimization of an interior permanent magnet motor“. Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116021.

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In recent years, due to growing environmental awareness regarding global warming, green cars, such as hybrid electric vehicles, have gained a lot of importance. With the decreasing cost of rare earth magnets, brushless permanent magnet motors, such as the Interior Permanent Magnet Motor, have found usage as part of the traction drive system in these types of vehicles. As a design issue, building a motor with a performance curve that suits both city and highway driving has been treated in this thesis as a multi-objective problem; matching specific points of the torque-speed curve to the desired performance output. Conventionally, this has been treated as separate problems or as a combination of several individual problems, but doing so gives little information about the trade-offs involved. As a means of identifying the compromising solutions, we have developed a stochastic optimizer for tackling electromagnetic device optimization and have also demonstrated a new innovative way of studying how different design parameters affect performance.

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Habib, Irfan. „Multi-objective optimisation of compute and data intensive e-science workflows“. Thesis, University of the West of England, Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573383.

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Abstract Raw e-Science data, which may be, for example, MRI brain scans, data from a high energy physics detector or metric data from the earth observation projects needs to undergo a series of computations before meaningful knowledge can be derived. One way to. de- scribe these series of computations on raw e-Science data are workflows. Workflows have emerged as the principle mechanism for describing and enacting complex e-Science analy- sis on distributed infrastructures such as Grids. Workflows provide domain scientists with a systematic, repeatable and reproducible means of conducting scientific analyses. Due to the demands of state-of-the-art e-Science applications, scientific workflows are increas- ing in complexity. This complexity is multi-dimensional; scientific workflows are scaling in terms of the number of computations and tasks they carry out. They are also scaling in terms of the data they manage and generate. Scientific workflows are also scaling in terms of the resources they consume. Due to all of these factors the optimisation of these workflows is a prime concern. State-of-the-art approaches to workflow optimisation pri- marily focus on compute optimisation. However, as e-Science is becoming increasingly data-centric, data optimisation is gaining increasing importance. This thesis explores the development of a multi-objective approach to the optimi- sation of scientific workflows. Differing and conflicting considerations are required to optimise a workflow for compute or data efficiency. The approach proposed formulates the optimisation of a scientific workflow as a multi-objective optimisation problem and demonstrates the optimisation of the same through the use of a multi-objective evolution- ary meta-heuristic. The results demonstrate that significant optimisation can be achieved through this approach.

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Mostaghim, Sanaz [Verfasser]. „Multi-Objective Evolutionary Algorithms : Data Structures, Convergence, and Diversity / Sanaz Mostaghim“. Aachen : Shaker, 2005. http://d-nb.info/1181620465/34.

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Ludick, Chantel Judith. „Disaggregating employment data to building level : a multi-objective optimisation approach“. Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/75596.

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The land use policies and development plans that are implemented in a city contribute to whether the city will be sustainable in the future. Therefore, when these policies are being established they should consider the potential impact on development. An analytical tool, such as land use change models, allow decision-makers to see the possible impact that these policies could have on development. Land use change models like UrbanSim make use of the relationship between households, buildings, and employment opportunities to model the decisions that people make on where to live and work. To be able to do this the model needs accurate data. When there is a more accurate location for the employment opportunities in an area, the decisions made by individuals can be better modelled and therefore the projected results are expected to be better. Previous research indicated that the methods that are traditionally used to disaggregate employment data to a lower level in UrbanSim projects are not applicable in the South African context. This is because the traditional methods require a detailed employment dataset for the disaggregation and this detailed employment dataset is not available in South Africa. The aim of this project was to develop a methodology for a metropolitan municipality in South Africa that could be used to disaggregate the employment data that is available at a higher level to a more detailed building level. To achieve this, the methodology consisted of two parts. The first part of the methodology was establishing a method that could be used to prepare a base dataset that is used for disaggregating the employment data. The second part of the methodology was using a multi-objective optimisation approach to allocate the number of employment opportunities within a municipality to building level. The algorithm was developed using the Distributed Evolutionary Algorithm in Python (DEAP) computational framework. DEAP is an open-source evolutionary algorithm framework that is developed in Python and enables users to rapidly create prototypes by allowing them to customise the algorithm to suit their needs The evaluation showed that it is possible to make use of multi-objective optimisation to disaggregate employment data to building level. The results indicate that the employment allocation algorithm was successful in disaggregating employment data from municipal level to building level. All evolutionary algorithms come with some degree of uncertainty as one of the main features of evolutionary algorithms is that they find the most optimal solution, and so there are other solutions available as well. Thus, the results of the algorithm also come with that same level of uncertainty. By enhancing the data used by land use change models, the performance of the overall model is improved. With this improved performance of the model, an improved view of the impact that land use policies could have on development can also be seen. This will allow decision-makers to draw the best possible conclusions and allow them the best possible opportunity to develop policies that will contribute to creating sustainable and lasting urban areas.
Dissertation (MSc (Geoinformatics))--University of Pretoria, 2020.
Geography, Geoinformatics and Meteorology
MSc (Geoinformatics)
Unrestricted

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Bücher zum Thema "Objective data"

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Coello, Carlos A. Coello. Swarm intelligence for multi-objective problems in data mining. Berlin: Springer Verlag, 2009.

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Coello, Carlos Artemio Coello, Satchidananda Dehuri und Susmita Ghosh, Hrsg. Swarm Intelligence for Multi-objective Problems in Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03625-5.

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A, Hillison William, Hrsg. Auditing & EDP: Objective questions and explanations. 5. Aufl. Gainesville, Fla: Gleim Publications, 1992.

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Lieberman, Elliot R. Multi-objective programming in the USSR. Boston: Academic Press, 1991.

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Gleim, Irvin N. Auditing & EDP: Objective questions and explanations. 4. Aufl. Gainesville, Fla: Gleim Publications, 1991.

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Gleim, Irvin N. Auditing & EDP: Objective questions and explanations. 2. Aufl. Gainesville, Fla: Accounting Publications, 1985.

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A, Hillison William, und Irwin Grady M, Hrsg. Auditing & EDP: Objective questions and explanations. 3. Aufl. Gainesville, Fla: Accounting Publications, 1988.

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Lieberman, Elliot R. Multi-objective programming in the USSR. Boston: Academic, 1991.

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Gleim, Irvin N. Auditing & systems: Objective questions and explanations. 7. Aufl. Gainesville, Fla: Gleim Publications, 1997.

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Franke, Richard H. Laplacian smoothing splines with generalized cross validation for objective analysis of meteorological data. Monterey, California: Naval Postgraduate School, 1985.

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Buchteile zum Thema "Objective data"

1

Dovey, James, und Ash Furrow. „Data Management with Core Data“. In Beginning Objective-C, 225–68. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4369-4_8.

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Campbell, Matthew. „Core Data“. In Objective-C Recipes, 339–408. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4372-4_10.

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Lee, Keith. „Foundation Functions and Data Types“. In Pro Objective-C, 239–52. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5051-7_13.

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Bennett, Gary, Mitch Fisher und Brad Lees. „Comparing Data“. In Objective-C for Absolute Beginners, 157–74. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2833-2_9.

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Bennett, Gary, Mitch Fisher und Brad Lees. „Comparing Data“. In Objective-C for Absolute Beginners, 199–214. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3654-2_9.

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Kaczmarek, Stefan, Brad Lees, Gary Bennett und Mitch Fisher. „Comparing Data“. In Objective-C for Absolute Beginners, 255–74. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3429-7_9.

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Bennett, Gary, Brad Lees und Mitchell Fisher. „Comparing Data“. In Objective-C for Absolute Beginners, 207–21. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1904-1_9.

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Dovey, James, und Ash Furrow. „Networking: Connections, Data, and the Cloud“. In Beginning Objective-C, 159–87. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4369-4_6.

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Mukhopadhyay, Anirban. „Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data“. In Multi-Objective Optimization, 55–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_3.

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Mallik, Saurav, Tapas Bhadra, Soumita Seth, Sanghamitra Bandyopadhyay und Jianjiao Chen. „Multi-Objective Optimization Approaches in Biological Learning System on Microarray Data“. In Multi-Objective Optimization, 159–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_7.

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Konferenzberichte zum Thema "Objective data"

1

Benouaret, Idir, Sihem Amer-Yahia, Christiane Kamdem-Kengne und Jalil Chagraoui. „A Bi-Objective Approach for Product Recommendations“. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006503.

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Itonaga, Makoto, Fumihiko Ito und Toshiya Saito. „Analysis of chromatic aberration of single objective lens and correction of that of a NA=0.85 objective lens.“ In Optical Data Storage. Washington, D.C.: OSA, 2003. http://dx.doi.org/10.1364/ods.2003.wa7.

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Burmester, G., und H. Rohler. „Objective-based Image Data Management“. In Second EAGE Borehole Geology Workshop. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201702393.

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4

Attaoui, Mohammed Oualid, Hanene Azzag, Mustapha Lebbah und Nabil Keskes. „Multi-objective data stream clustering“. In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389930.

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5

Zheng, Yong, und David (Xuejun) Wang. „Multi-Objective Recommendations“. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3470788.

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6

Jalali, Leila, Misbah Khan und Rahul Biswas. „Learning and Multi-Objective Optimization for Automatic Identity Linkage“. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622581.

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7

Pietri, Ilia, Yannis Chronis und Yannis Ioannidis. „Multi-objective optimization of scheduling dataflows on heterogeneous cloud resources“. In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257946.

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8

Zeng, Fanchao, James Decraene, Malcolm Yoke Hean Low, Cai Wentong, Philip Hingston und Suiping Zhou. „High-dimensional objective-based data farming“. In 2011 Ieee Symposium On Computational Intelligence For Security And Defence Applications - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cisda.2011.5945942.

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9

Antony, Shyam, Ping Wu, Divyakant Agrawal und Amr El Abbadi. „MOOLAP: Towards Multi-Objective OLAP“. In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497567.

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Shi, Chuan, Xiangnan Kong, Philip S. Yu und Bai Wang. „Multi-Objective Multi-Label Classification“. In Proceedings of the 2012 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611972825.31.

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Berichte der Organisationen zum Thema "Objective data"

1

Hunt, J. W. Tank safety screening data quality objective. Revision 1. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/61107.

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2

Gydesen, S. Status of document search and data quality objective efforts. Office of Scientific and Technical Information (OSTI), Oktober 1992. http://dx.doi.org/10.2172/10107852.

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3

Gydesen, S. Status of document search and data quality objective efforts. Office of Scientific and Technical Information (OSTI), Oktober 1992. http://dx.doi.org/10.2172/6992873.

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4

Irvine, Nelson. Analysis of the Objective Data From Fleet Battle Experiment Hotel. Fort Belvoir, VA: Defense Technical Information Center, Januar 2001. http://dx.doi.org/10.21236/ada389211.

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5

Irvine, Nelson. Objective Data from Fleet Battle Experiment Foxtrot, Golf, and Hotel. Fort Belvoir, VA: Defense Technical Information Center, Januar 2001. http://dx.doi.org/10.21236/ada389356.

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6

Gates, C. M., und M. R. Beckette. Identification of physical properties for the retrieval data quality objective process. Office of Scientific and Technical Information (OSTI), Juni 1995. http://dx.doi.org/10.2172/86993.

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7

Tung, S. L. Users Guide for Normal Mode Objective Analysis of Global Data Assimilation,. Fort Belvoir, VA: Defense Technical Information Center, März 1985. http://dx.doi.org/10.21236/ada160373.

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8

Meacham, J. E. Data quality objective to support resolution of the organic solvent safety issue. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/341237.

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9

Kirkbride, R. A. Technical work plan for the privatization waste characterization data quality objective process. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/341255.

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10

Mulkey, C. H. ,. Westinghouse Hanford. Data quality objective for regulatory requirements for dangerous waste sampling and analysis. Office of Scientific and Technical Information (OSTI), Juli 1996. http://dx.doi.org/10.2172/659219.

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