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 29. Juli 2025

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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 Pe

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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 alte

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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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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 gro

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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

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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 ran

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Candilasa, Jahzeel M., and Kent T. Onahon. "Global University Rankings: Characterization of Higher Education, Institution’s Competitiveness." International Journal of Research and Innovation in Social Science VIII, no. XII (2025): 3267–79. https://doi.org/10.47772/ijriss.2024.8120270.

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Global university rankings serve as information tool for students in choosing their studies, whereas institutional leaders regard them as a management and strategic decision-making tool. However, little is known about the relevance of global university rankings for developing countries like the Philippines. Using cluster analysis and stepwise regression, this study investigated the competitiveness indicators among clusters of top-ranking universities from three different global university-ranking organizations namely. Furthermore, it also created a best-fit model that accurately predicts insti

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Biolcati Rinaldi, Ferruccio, Daniele Checchi, Chiara Guglielmetti, Silvia Salini, and Matteo Turri. "Ranking e valutazione: il caso delle classifiche delle universitŕ." RIV Rassegna Italiana di Valutazione, no. 41 (May 2009): 81–114. http://dx.doi.org/10.3280/riv2008-041006.

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- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such

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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

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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 e

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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)

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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 explosiv

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

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

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Kevin, Boyle, and Charles Sturt University. Faculty of Commerce, eds. Comparative reliability of rank and choice data in stated preference models. Faculty of Commerce, Charles Sturt University, 2001.

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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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Muwanga-Zake, E. S. K. A framework for ranking data needs: A case study of the Coffee Rehabilitation Project in Uganda. School of Development Studies, University of East Anglia, 1986.

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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"

Loukeris, Nikolaos. "HEterogenous RAnking CummuLativE System - HERACLES." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800472.

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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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Berichte der Organisationen zum Thema "Ranking data"

Rose, Melissa. Quality Ranking System for Molten Salt Thermal Property Data. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1971981.

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Unknown, Author. L52043 SCC Initiation Susceptibility Ranking-Screening. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0011149.

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The objectives of this project were: To increase the understanding of the metallurgical factors controlling low pH SCC initiation and crack propagation; To extend and enlarge the pool of test data relating to linepipe low-pH SCC susceptibility and to generate data to enable comparison of European and North American materials; and To further verify the standardised test procedures developed by EPRG for investigating crack initiation and crack propagation.

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Rose, Melissa. Quality Ranking of Unary Fluoride Salt Property Data in MSTDB-TP. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2278997.

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Rose, Melissa. Quality Ranking of Unary Chloride Salt Property Data Included in MSTDB-TP. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2545095.

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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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Young, David, Brandan Scully, Sean McGill, Ashley Elkins, and Marin Kress. Ranking ports by vessel demand for depth. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/48019.

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The US Army Corps of Engineers (USACE) traditionally uses two metrics to evaluate the maintenance of coastal navigation projects: tonnage at the associated port (representing relative importance) and the controlling depth in the channel (representing operating condition). These are incorporated into a risk-based decision framework directing funds where channel conditions have deteriorated and the disrupted tonnage potential is the highest. However, these metrics fail to capture shipper demand for the maintained depth service provided by the USACE through dredging. Using automatic identificatio

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Pălici, Bogdan, Alin Savu, Maria Trifon, et al. Mapping - Interactive Tool for Exploring Statistical Data about Cultural Infrastructure in Romania. National Institute for Cultural Research and Training, 2021. http://dx.doi.org/10.61789/mod.cdi.crtg.en.21.

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The Cartographic Module is an instrument for data selection, structuring, visualisation and analysis. It provides for multiple selection functionalities both at geographical area level (county, locality) and at data level (category of indicators and indicators). Three types of dynamic graphs are available on the indicator pages: comparison/evolution, ranking and frequency.

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Johnson, Derek, and Nigel Clark. PR-746-22602-R01 In-situ Cost-Effective Detection and Quantification of Emission from Vents. Pipeline Research Council International, Inc. (PRCI), 2023. http://dx.doi.org/10.55274/r0000039.

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The Pipeline Research Council International, Inc. (PRCI) seeks to reduce greenhouse gas (GHG) from leaks and equipment emissions from all parts of the hydrocarbon transport and storage infrastructure. Specifically, there is a need to identify and compare cost-effective technologies that can detect and quantify leaks or emissions from compressor rod packing and seals, scrubber dumps, and blowdown valves. Furthermore, regulations are increasingly prescriptive and are moving from conventional leak detection and repair (LDAR) programs to direct quantification and/or period/continuous monitoring of

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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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