Thematische Bibliographien / Ranking data

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Auswahl der wissenschaftlichen Literatur zum Thema „Ranking data“

Autor: Grafiati

Veröffentlicht am 1. Juli 2021

Zuletzt aktualisiert am 10. März 2023

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Zeitschriftenartikel zum Thema "Ranking data"

Banihashemi, Sayyid Ali, and Mohammad Khalilzadeh. "A new approach for ranking efficient DMUs with data envelopment analysis." World Journal of Engineering 17, no. 4 (2020): 573–83. http://dx.doi.org/10.1108/wje-03-2020-0092.

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Purpose Classical models of data envelopment analysis (DEA) calculate the efficiency of decision-making units do not differentiate between efficient units. The purpose of this paper is to present a new method for ranking efficient units and compare it with the other methods presented in this field. Design/methodology/approach In this paper, a new method is presented for ranking efficient units. To validate the proposed method, a real case, which was studied by Li et al. (2016) is examined and the rankings of the efficient units are compared with four other methods including the Andersen and Petersen’s super-efficiency, game theory and the concept of Shapley value and the technique for order of preference by similarity to ideal solution methods. Findings The results show that there is a high correlation between the rankings of efficient units obtained by the new proposed method and the other methods such as Andersen and Petersen’s super-efficiency, game theory and Shapley value methods. Originality/value The problem of ranking efficient units with the DEA method is an important issue for researchers. Extensive studies have been proposed to provide methods for ranking efficient units. This paper proposes a simple and fast method for ranking efficient units that achieves better results.

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Zar, Jerrold H. "Ranking data with BASIC." Behavior Research Methods, Instruments, & Computers 17, no. 1 (1985): 142. http://dx.doi.org/10.3758/bf03200918.

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Jestes, Jeffrey, Jeff M. Phillips, Feifei Li, and Mingwang Tang. "Ranking large temporal data." Proceedings of the VLDB Endowment 5, no. 11 (2012): 1412–23. http://dx.doi.org/10.14778/2350229.2350257.

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Grossman, J. P., and Gregory Minton. "Inversions in ranking data." Discrete Mathematics 309, no. 20 (2009): 6149–51. http://dx.doi.org/10.1016/j.disc.2009.04.030.

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Ivanov, A. A., and N. P. Yashina. "Big Data Analysis in Multi-Criteria Choice Problems." Моделирование и анализ данных 12, no. 2 (2022): 5–19. http://dx.doi.org/10.17759/mda.2022120201.

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The problem of multi-criteria choice with non-uniform scales of criteria is considered. A model of a multicriteria choice problem is described, the main elements of which are sets of alternatives and quality criteria, as well as algorithms that allow ranking alternatives without prior reduction of the criteria scales to homogeneous ones. Algorithms for constructing aggregated ranking of alternatives are based on the construction of preference matrices by criteria containing information on the degree of superiority of one alternative over another. Propositions are proved that allow ranking alternatives with assessments according to two quality criteria. Algorithms for indexing alternatives are proposed that allow ranking alternatives for an arbitrary number of criteria. The best aggregated ranking is determined by the total distance to the rankings of alternatives by criteria. All algorithms have polynomial computational complexity, which makes it possible to work with large arrays of initial information. A software system for ranking alternatives in problems with big data has been developed. The initial information is stored in Excel tables, which makes it easy to take into account the limitations on the criteria scales. The operation of the software system is demonstrated by the example of choosing the best version of a drone for purchase in order to observe the terrain, shoot it and transmit information to the operator.

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Doğan, Güleda, and Umut Al. "Is it possible to rank universities using fewer indicators? A study on five international university rankings." Aslib Journal of Information Management 71, no. 1 (2019): 18–37. http://dx.doi.org/10.1108/ajim-05-2018-0118.

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Purpose The purpose of this paper is to analyze the similarity of intra-indicators used in research-focused international university rankings (Academic Ranking of World Universities (ARWU), NTU, University Ranking by Academic Performance (URAP), Quacquarelli Symonds (QS) and Round University Ranking (RUR)) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: At what level are the intra-indicators used in international university rankings similar? Is it possible to group intra-indicators according to their similarities? What is the effect of similar intra-indicators on overall rankings? Design/methodology/approach Indicator-based scores of all universities in five research-focused international university rankings for all years they ranked form the data set of this study for the first and second research questions. The authors used a multidimensional scaling (MDS) and cosine similarity measure to analyze similarity of indicators and to answer these two research questions. Indicator-based scores and overall ranking scores for 2015 are used as data and Spearman correlation test is applied to answer the third research question. Findings Results of the analyses show that the intra-indicators used in ARWU, NTU and URAP are highly similar and that they can be grouped according to their similarities. The authors also examined the effect of similar indicators on 2015 overall ranking lists for these three rankings. NTU and URAP are affected least from the omitted similar indicators, which means it is possible for these two rankings to create very similar overall ranking lists to the existing overall ranking using fewer indicators. Research limitations/implications CWTS, Mapping Scientific Excellence, Nature Index, and SCImago Institutions Rankings (until 2015) are not included in the scope of this paper, since they do not create overall ranking lists. Likewise, Times Higher Education, CWUR and US are not included because of not presenting indicator-based scores. Required data were not accessible for QS for 2010 and 2011. Moreover, although QS ranks more than 700 universities, only first 400 universities in 2012–2015 rankings were able to be analyzed. Although QS’s and RUR’s data were analyzed in this study, it was statistically not possible to reach any conclusion for these two rankings. Practical implications The results of this study may be considered mainly by ranking bodies, policy- and decision-makers. The ranking bodies may use the results to review the indicators they use, to decide on which indicators to use in their rankings, and to question if it is necessary to continue overall rankings. Policy- and decision-makers may also benefit from the results of this study by thinking of giving up using overall ranking results as an important input in their decisions and policies. Originality/value This study is the first to use a MDS and cosine similarity measure for revealing the similarity of indicators. Ranking data is skewed that require conducting nonparametric statistical analysis; therefore, MDS is used. The study covers all ranking years and all universities in the ranking lists, and is different from the similar studies in the literature that analyze data for shorter time intervals and top-ranked universities in the ranking lists. It can be said that the similarity of intra-indicators for URAP, NTU and RUR is analyzed for the first time in this study, based on the literature review.

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Wang, Jingyan, and Nihar B. Shah. "Ranking and Rating Rankings and Ratings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13704–7. http://dx.doi.org/10.1609/aaai.v34i09.7126.

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Cardinal scores collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are typically far more complex and not well understood. It is widely believed that in the absence of simplifying assumptions on the miscalibration, the only useful information in practice from the cardinal scores is the induced ranking. In this paper we address the fundamental question of whether this widespread folklore belief is actually true. We consider cardinal scores with arbitrary (or even adversarially chosen) miscalibrations that is only required to be consistent with the induced ranking. We design rating-based estimators and prove that despite making no assumptions on the ratings, they strictly and uniformly outperform all possible estimators that rely on only the ranking. These estimators can be used as a plug-in to show the superiority of cardinal scores over ordinal rankings for a variety of applications, including A/B testing and ranking. This work thus provides novel fundamental insights in the eternal debate between cardinal and ordinal data: It ranks the approach of using ratings higher than that of using rankings, and rates both approaches in terms of their estimation errors.

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Rashid, Mahbub. "DesignIntelligence and the Ranking of Professional Architecture Programs: Issues, Impacts, and Suggestions." Architecture 2, no. 3 (2022): 593–615. http://dx.doi.org/10.3390/architecture2030032.

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This paper studies the annual rankings of professional architectural degree programs by DesignIntelligence (DI). It uses a literature review and the statistical analysis of DI rankings and program-specific data to explore the limitations of the ranking system and its impacts on programs and public opinion. According to the findings of the study, the limitations of this system are related to the data it uses, the methods it uses to collect the data, and the way it uses the data for ranking purposes. Still, the ranking system can force architectural programs into a costly campaign for better ranks. It can also mislead prospective students in choosing programs that may not match their expectations. Additionally, it does not provide a reliable assessment of the capacity of a program to serve the profession and produce public good. It is suggested that a more objective, reliable, and relevant ranking system is needed for professional architecture degree programs. For this, the ranking system should emphasize criteria and methods different from the current DI system of rankings and should allow users to personalize rankings based on their perspectives, needs, and priorities.

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Alvo, Mayer, and Kadir Ertas. "Graphical methods for ranking data." Canadian Journal of Statistics 20, no. 4 (1992): 469–82. http://dx.doi.org/10.2307/3315616.

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Brady, Henry E. "Dimensional Analysis of Ranking Data." American Journal of Political Science 34, no. 4 (1990): 1017. http://dx.doi.org/10.2307/2111470.

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Dissertationen zum Thema "Ranking data"

Vanichbuncha, Tita. "Modelling partial ranking data." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/66664/.

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Ranking is one of the most used methods in not only in statistics but also in other field such as computer science and psychology. This method helps us determine order of objects in a group such as preference of animal species, and has very broad applications. However, when the number of objects to be ranked becomes larger, the uncertainty of the ranking typically increases since it is harder for the ranker to express their preference accurately. This leads to the idea of partial ranking which allows rankers to rank just a subset of objects in the group and then combine their results together to form the global ranking. This thesis focuses on this type of data. The main challenge is how to accurately analyze partially ranked data and decide the global ranking. There are several models that address this kind of problem such as the Bradley-Terry (BT) model and the Plackett-Luce (PL) model. The BT model is for paired comparisons while the PL model is for any number of ranked objects. The PL model is slow to fit using existing R packages. We implement the algorithms in R and do empirical studies using simulated data. The results show that our algorithms perform faster than the existing packages in R. We also implement R code for computing the observed information matrix. Rank-breaking methods are also considered in order to be able to use the BT model with different weightings instead of using the PL model. We examine the performance of various weightings by experimental studies with the simulated data and with real-world data. Our BTw-Sqrt weighting performs best when the number of rankers is small. In order to choose subsets of objects to be ranked, we consider three existing criteria which are D-optimality, E-optimality, and Wald and we propose three new methods. Experiments have been done using simulated data and the results compared with random selection. Our result shows that the existing criteria sometimes perform better than random selection. Our proposed methods usually ensure that the PL model can be fitted to data from fewer rankers than random selection. We describe two extensions of the PL model, the Rank-Ordered Logit (ROL) model and the Benter model. The ROL model extends the PL model by allowing covariates to be incorporated and the Benter model allows preferences for higher-ranked items to be stronger than for lower-ranked items. Both extensions improve the fit of the PL model to an example dataset when using the Likelihood Ratio (LR) test to compare models. We combine these two extensions to give a model that incorporates covariates and allows for a dampening effect. The combined model further improves the fit to our example data when compared with the ROL model by using LR test. We implement R codes for analyzing and computing the observed information matrices of the ROL, Benter, and combined models. We also explore another type of partial ranking data where individuals are allowed to mention any objects rather than being given a predefined list of objects to rank. We consider the idea of Participatory Risk Mapping (PRM) which provides severity and incidence scores. The severity and incidence scores can be modelled using the PL model and a new proposed model, respectively.

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Lo, Siu-ming, and 盧小皿. "Factor analysis for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B30162464.

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Lo, Siu-ming. "Factor analysis for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20792967.

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林漢坤 and Hon-kwan Lam. "Analysis of ranking data with covariates." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31215476.

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Qi, Fang, and 齊放. "Some topics in modeling ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209210.

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Many applications of analysis of ranking data arise from different fields of study, such as psychology, economics, and politics. Over the past decade, many ranking data models have been proposed. AdaBoost is proved to be a very successful technique to generate a stronger classifier from weak ones; it can be viewed as a forward stagewise additive modeling using the exponential loss function. Motivated by this, a new AdaBoost algorithm is developed for ranking data. Taking into consideration the ordinal structure of the ranking data, I propose measures based on the Spearman/Kendall distance to evaluate classifier instead of the usual misclassification rate. Some ranking datasets are tested by the new algorithm, and the results show that the new algorithm outperforms traditional algorithms. The distance-based model assumes that the probability of observing a ranking depends on the distance between the ranking and its central ranking. Prediction of ranking data can be made by combining distance-based model with the famous k-nearest-neighbor (kNN) method. This model can be improved by assigning weights to the neighbors according to their distances to the central ranking and assigning weights to the features according to their relative importance. For the feature weighting part, a revised version of the traditional ReliefF algorithm is proposed. From the experimental results we can see that the new algorithm is more suitable for ranking data problem. Error-correcting output codes (ECOC) is widely used in solving multi-class learning problems by decomposing the multi-class problem into several binary classification problems. Several ECOCs for ranking data are proposed and tested. By combining these ECOCs and some traditional binary classifiers, a predictive model for ranking data with high accuracy can be made. While the mixture of factor analyzers (MFA) is useful tool for analyzing heterogeneous data, it cannot be directly used for ranking data due to the special discrete ordinal structures of rankings. I fill in this gap by extending MFA to accommodate for complete and incomplete/partial ranking data. Both simulated and real examples are studied to illustrate the effectiveness of the proposed MFA methods. published_or_final_version Statistics and Actuarial Science Doctoral Doctor of Philosophy

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Lam, Hon-kwan. "Analysis of ranking data with covariates /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19943313.

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Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.

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Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.

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Lee, Hong, and 李匡. "Model-based decision trees for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45149707.

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Korba, Anna. "Learning from ranking data : theory and methods." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009/document.

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Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classements Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages

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Gregory, Machon. "Shape identication and ranking in temporal data sets." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9319.

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Thesis (M.S.) -- University of Maryland, College Park, 2009. Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Bücher zum Thema "Ranking data"

Hua, Ming, and Jian Pei. Ranking Queries on Uncertain Data. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9380-9.

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Alvo, Mayer, and Philip L. H. Yu. Statistical Methods for Ranking Data. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1471-5.

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D, Pei Jian Ph, ed. Ranking queries on uncertain data. Springer, 2011.

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Yu, Philip. L. H., author, ed. Statistical methods for ranking data. Springer, 2014.

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Fligner, Michael A., and Joseph S. Verducci, eds. Probability Models and Statistical Analyses for Ranking Data. Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-2738-0.

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Analyzing and modeling rank data. Chapman & Hall, 1995.

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Critchlow, Douglas E. Metric methods for analyzing partially ranked data. Springer-Verlag, 1985.

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Metric methods for analyzing partially ranked data. Springer-Verlag, 1985.

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Moskowitz, Daniel B. Ranking hospitals and physicians: The use and misuse of performance data. Faulkner & Gray's Health Information Center, 1994.

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Schulz, E. Matthew. Controlling for rater effects when comparing survey items with incomplete Likert data. ACT, Inc., 2001.

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Buchteile zum Thema "Ranking data"

Shikhman, Vladimir, and David Müller. "Ranking." In Mathematical Foundations of Big Data Analytics. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62521-7_1.

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Jin, Wei, Danhuai Guo, Li-kun Zhao, and Ji-Chao Li. "Performance Ranking Based on Bézier Ranking Principal Curve." In Spatial Data and Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69873-7_15.

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Wu, Shengli. "Ranking-Based Fusion." In Data Fusion in Information Retrieval. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28866-1_7.

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Huang, Shuai, and Houtao Deng. "Recognition Logistic Regression & Ranking." In Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102656-ch3.

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Rendle, Steffen. "Ranking from Incomplete Data." In Context-Aware Ranking with Factorization Models. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16898-7_3.

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Alvo, Mayer, and Philip L. H. Yu. "Exploratory Analysis of Ranking Data." In Statistical Methods for Ranking Data. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1471-5_2.

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Alvo, Mayer, and Philip L. H. Yu. "Probability Models for Ranking Data." In Statistical Methods for Ranking Data. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1471-5_8.

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Alvo, Mayer, and Philip L. H. Yu. "Probit Models for Ranking Data." In Statistical Methods for Ranking Data. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1471-5_9.

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Hua, Ming, and Jian Pei. "Ranking Queries on Probabilistic Linkages." In Ranking Queries on Uncertain Data. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9380-9_7.

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Khalid, Majdi, Indrakshi Ray, and Hamidreza Chitsaz. "Confidence-Weighted Bipartite Ranking." In Advanced Data Mining and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_3.

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Konferenzberichte zum Thema "Ranking data"

Prasad, Ananth Krishna, Morteza Rezaalipour, Masoud Dehyadegari, and Mahdi Nazm Bojnordi. "Memristive Data Ranking." In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2021. http://dx.doi.org/10.1109/hpca51647.2021.00045.

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Xin, Dong, and Jiawei Han. "Integrating OLAP and Ranking: The Ranking-Cube Methodology." In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 2007. http://dx.doi.org/10.1109/icdew.2007.4401000.

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Yakout, Mohamed, Ahmed K. Elmagarmid, and Jennifer Neville. "Ranking for data repairs." In 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). IEEE, 2010. http://dx.doi.org/10.1109/icdew.2010.5452767.

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Li, Feifei, Ke Yi, and Jeffrey Jestes. "Ranking distributed probabilistic data." In the 35th SIGMOD international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1559845.1559885.

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Agarwal, Shivani. "Ranking on graph data." In the 23rd international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143848.

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Long, Bo, Yi Chang, Srinivas Vadrevu, Shuang Hong Yang, and Zhaohui Zheng. "Ranking with auxiliary data." In the 19th ACM international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1871437.1871654.

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Wang, Yue, Dawei Yin, Luo Jie, et al. "Beyond Ranking." In WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2835776.2835824.

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Fenwick, P. "Symbol ranking text compressors." In Proceedings DCC '97. Data Compression Conference. IEEE, 1997. http://dx.doi.org/10.1109/dcc.1997.582093.

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Chareyron, Gael, Berengere Branchet, and Sebastien Jacquot. "A new area tourist ranking method." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364126.

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Agrawal, Rajeev, William I. Grosky, and Farshad Fotouhi. "Ranking Privacy Policy." In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 2007. http://dx.doi.org/10.1109/icdew.2007.4400991.

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Berichte der Organisationen zum Thema "Ranking data"

Biersdorf, John, Ha Bui, Tatsuya Sakurahara, et al. Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1632319.

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Bazylik, Sergei, Magne Mogstad, Joseph Romano, Azeem Shaikh, and Daniel Wilhelm. Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29519.

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Jette, S. J., D. A. Lamar, T. J. McLaughlin, et al. Hazard ranking system evaluation of CERCLA inactive waste sites at Hanford: Volume 3: Unplanned-release sites (HISS data base). Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/6560414.

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Jette, S. J., D. A. Lamar, T. J. McLaughlin, et al. Hazard ranking system evaluation of CERCLA inactive waste sites at Hanford: Volume 2: Engineered-facility sites (HISS data base). Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/6574546.

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Ghasemi, H., and T. Allen. Selection and ranking of groundmotion models for the 2018 National Seismic Hazard Assessment of Australia: summary of ground-motion data, methodology and outcomes. Geoscience Australia, 2018. http://dx.doi.org/10.11636/record.2018.029.

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Wu, Ling, Tao Zhang, Yao Wang, et al. Polymorphisms and premature ovarian insufficiency and failure: A comprehensive meta-analysis update, subgroup, ranking, and network analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.1.0052.

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Review question / Objective: Early identification of women potentially who develop POI and POF is essential for early screening and treatment to improve clinical outcomes. We aim to conduct a comprehensive meta-analysis update, subgroup, ranking and network analysis for all available genetic polymorphism and associated with the POI and POF risk. Information sources: Six electronic databases will be included such as PubMed, Web of Science, Embase, MEDLINE, WANFANG DATA, CNKI. Will contact with authors by emails when necessary.

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Aiken, Catherine, James Dunham, and Remco Zwetsloot. Immigration Pathways and Plans of AI Talent. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20200013.

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To better understand immigration paths of the AI workforce, CSET surveyed recent PhD graduates from top-ranking AI programs at U.S. universities. This data brief offers takeaways — namely, that AI PhDs find the United States an appealing destination for study and work, and those working in the country plan to stay.

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Shinn, J. H., S. A. Martins, P. L. Cederwall, and L. B. Gratt. Smokes and obscurants: A health and environmental effects data base assessment: A first-order, environmental screening and ranking of Army smokes and obscurants: Phase 1 report. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/6068996.

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Shapovalov, Yevhenii B., Viktor B. Shapovalov, and Vladimir I. Zaselskiy. TODOS as digital science-support environment to provide STEM-education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3250.

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The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been worked out. These systems provide high performance of information management of scientific information.

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Mayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, 2019. http://dx.doi.org/10.53328/guxy9244.

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Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities

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