Relevant bibliographies by topics / Understanding of data models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Understanding of data models'

Author: Grafiati
Published: 17 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Understanding of data models"

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Shanks, Graeme, and Peta Darke. "Understanding corporate data models." Information & Management 35, no. 1 (1999): 19–30. http://dx.doi.org/10.1016/s0378-7206(98)00078-0.

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French, Robert M., and Maud Jacquet. "Understanding bilingual memory: models and data." Trends in Cognitive Sciences 8, no. 2 (2004): 87–93. http://dx.doi.org/10.1016/j.tics.2003.12.011.

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DeBruine, Lisa M., and Dale J. Barr. "Understanding Mixed-Effects Models Through Data Simulation." Advances in Methods and Practices in Psychological Science 4, no. 1 (2021): 251524592096511. http://dx.doi.org/10.1177/2515245920965119.

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Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this research is analyzed using analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data
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Knüsel, Benedikt, and Christoph Baumberger. "Understanding climate phenomena with data-driven models." Studies in History and Philosophy of Science Part A 84 (December 2020): 46–56. http://dx.doi.org/10.1016/j.shpsa.2020.08.003.

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Durant, Szonya. "Zhaoping, L. Understanding Vision: Theory, Models, and Data." Perception 45, no. 10 (2016): 1207–8. http://dx.doi.org/10.1177/0301006616660638.

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Johora, Fatema Tuz, Mia Md Tofayel Gonee Manik, Afia Fairooz Tasnim, Sadia Islam Nilima, and Rakibul Hasan. "Advanced-Data Analytics for Understanding Biochemical Pathway Models." American Journal of Computing and Engineering 4, no. 2 (2024): 21–34. http://dx.doi.org/10.47672/ajce.2451.

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Purpose: The article delves into the biochemical and metabolic processes, highlighting the importance of data analysis in system biology, drug investigations, illnesses, diagnostics, and therapies, highlighting the necessity of data collection and interpretation. Materials and Methods: The data and sample is collected by analyzing several articles using a simple collective method. Findings: Key pathways for cell respiration include glycolysis, Krebs cycle, and oxidative phosphorylation, which can cause metabolic disorders like diabetes or cancer. Advanced data processing methods and biochemica
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Best, Nicky, and Peter Green. "Structure and uncertainty: Graphical models for understanding complex data." Significance 2, no. 4 (2005): 177–81. http://dx.doi.org/10.1111/j.1740-9713.2005.00133.x.

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Steinberg, David M., and Dizza Bursztyn. "Data Analytic Tools for Understanding Random Field Regression Models." Technometrics 46, no. 4 (2004): 411–20. http://dx.doi.org/10.1198/004017004000000419.

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Cagetti, Marco, and Mariacristina De Nardi. "WEALTH INEQUALITY: DATA AND MODELS." Macroeconomic Dynamics 12, S2 (2008): 285–313. http://dx.doi.org/10.1017/s1365100507070150.

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In the United States wealth is highly concentrated and very unequally distributed: the richest 1% hold one third of the total wealth in the economy. Understanding the determinants of wealth inequality is a challenge for many economic models. We summarize some key facts about the wealth distribution and what economic models have been able to explain so far.
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Butts, Daniel A. "Data-Driven Approaches to Understanding Visual Neuron Activity." Annual Review of Vision Science 5, no. 1 (2019): 451–77. http://dx.doi.org/10.1146/annurev-vision-091718-014731.

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With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However,
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Dissertations / Theses on the topic "Understanding of data models"

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Sommeria-Klein, Guilhem. "From models to data : understanding biodiversity patterns from environmental DNA data." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30390/document.

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La distribution de l'abondance des espèces en un site, et la similarité de la composition taxonomique d'un site à l'autre, sont deux mesures de la biodiversité ayant servi de longue date de base empirique aux écologues pour tenter d'établir les règles générales gouvernant l'assemblage des communautés d'organismes. Pour ce type de mesures intégratives, le séquençage haut-débit d'ADN prélevé dans l'environnement (" ADN environnemental ") représente une alternative récente et prometteuse aux observations naturalistes traditionnelles. Cette approche présente l'avantage d'être rapide et standardisé
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Kivinen, Jyri Juhani. "Statistical models for natural scene data." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8879.

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This thesis considers statistical modelling of natural image data. Obtaining advances in this field can have significant impact for both engineering applications, and for the understanding of the human visual system. Several recent advances in natural image modelling have been obtained with the use of unsupervised feature learning. We consider a class of such models, restricted Boltzmann machines (RBMs), used in many recent state-of-the-art image models. We develop extensions of these stochastic artificial neural networks, and use them as a basis for building more effective image models, and t
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Steinberg, Daniel. "An Unsupervised Approach to Modelling Visual Data." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9415.

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For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automati
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Das, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.

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Computer and Information Science<br>Ph.D.<br>Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommoda
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LaMar, Michelle Marie. "Models for understanding student thinking using data from complex computerized science tasks." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686374.

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<p> The Next Generation Science Standards (NGSS Lead States, 2013) define performance targets which will require assessment tasks that can integrate discipline knowledge and cross-cutting ideas with the practices of science. Complex computerized tasks will likely play a large role in assessing these standards, but many questions remain about how best to make use of such tasks within a psychometric framework (National Research Council, 2014). This dissertation explores the use of a more extensive cognitive modeling approach, driven by the extra information contained in action data collected wh
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Maloo, Akshay. "Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25296.

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Dynamic Behavior Visualizer (DBV) is a visual analytics environment to visualize the spatial and temporal movements and behavioral changes of an individual or a group, e.g. family within a realistic urban environment. DBV is specifically designed to visualize the adaptive behavioral changes, as they pertain to the interactions with multiple inter-dependent infrastructures, in the aftermath of a large crisis, e.g. hurricane or the detonation of an improvised nuclear device. DBV is web-enabled and thus is easily accessible to any user with access to a web browser. A novel aspect of the system is
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Izumi, Kenji. "Application of Paleoenvironmental Data for Testing Climate Models and Understanding Past and Future Climate Variations." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18510.

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Paleo data-model comparison is the process of comparing output from model simulations of past periods with paleoenvironmental data. It enables us to understand both the paleoclimate mechanism and responses of the earth environment to the climate and to evaluate how models work. This dissertation has two parts that each involve the development and application of approaches for data-model comparisons. In part 1, which is focused on the understanding of both past and future climatic changes/variations, I compare paleoclimate and historical simulations with future climate projections exploiting th
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Lipecki, Johan, and Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.

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This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more
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Abufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.

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Wojatzki, Michael Maximilian [Verfasser], and Torsten [Akademischer Betreuer] Zesch. "Computer-assisted understanding of stance in social media : formalizations, data creation, and prediction models / Michael Maximilian Wojatzki ; Betreuer: Torsten Zesch." Duisburg, 2019. http://d-nb.info/1177681471/34.

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Books on the topic "Understanding of data models"

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Ashwin, Ram, and Moorman Kenneth, eds. Understanding language understanding: Computational models of reading. MIT Press, 1999.

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Klüver, Jürgen. Social Understanding: On Hermeneutics, Geometrical Models and Artificial Intelligence. Springer Science+Business Media B.V., 2011.

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F, Robinson Byron, ed. Understanding log-linear analysis with ILOG: An interactive approach. L. Erlbaum, 1994.

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Berry, Joseph K. Map analysis: Understanding spatial patterns and relationships. GeoTec Media, 2007.

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Zoran, Stančič, and Veljanovski Tatjana, eds. Computing archaeology for understanding the past: CAA 2000 : computer applications and quantitative methods in archaeology : proceedings of the 28th Conference, Ljubljana, April 2000. Archaeopress, 2001.

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1941-, Taylor Arlene G., ed. Understanding FRBR: What it is and how it will affect our retrieval tools. Libraries Unlimited, 2007.

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Erickson, Bonnie H. Understanding data. 2nd ed. University of Toronto Press, 1992.

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A, Nosanchuk T., ed. Understanding data. 2nd ed. Open University Press, 1992.

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Winkelmann, Rainer. Count Data Models. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-21735-1.

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Pivert, Olivier, ed. NoSQL Data Models. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119528227.

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Book chapters on the topic "Understanding of data models"

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Westfall, Peter H., and Andrea L. Arias. "Censored Data Models." In Understanding Regression Analysis. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-15.

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David, Salsburg. "5 Models Versus Data." In Understanding Randomness. CRC Press, 2017. http://dx.doi.org/10.1201/9780203734674-7.

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Soh, Julian, and Priyanshi Singh. "Understanding Large Language Models." In Data Science Solutions on Azure. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0914-9_2.

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Holloway, Paul. "Spatial Data Models." In Understanding GIS through Sustainable Development Goals. CRC Press, 2023. http://dx.doi.org/10.1201/9781003220510-5.

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Matthews, David Edward, and Vernon Todd Farewell. "12 Regression Models for Count Data." In Using and Understanding Medical Statistics. KARGER, 2007. http://dx.doi.org/10.1159/000099427.

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Badiru, Adedeji B. "Data Analytics Tools for Understanding Random Field Regression Models *." In Data Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146-7.

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Hand, David J. "Intelligent Data Analysis and Deep Understanding." In Causal Models and Intelligent Data Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_5.

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Jung, Dominik. "Business Data Understanding." In The Modern Business Data Analyst. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59907-1_3.

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Matthews, David Edward, and Vernon Todd Farewell. "10 Linear Regression Models for Medical Data." In Using and Understanding Medical Statistics. KARGER, 2007. http://dx.doi.org/10.1159/000099425.

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Liiv, Innar. "Understanding the Data Model." In Behaviormetrics: Quantitative Approaches to Human Behavior. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2418-6_1.

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Conference papers on the topic "Understanding of data models"

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Wood, Jacob, and Deepti Joshi. "Conflict-RAG: Understanding Evolving Conflicts Using Large Language Models." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825676.

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Sood, Palak, Chengyang He, Divyanshu Gupta, Yue Ning, and Ping Wang. "Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826043.

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Xue, Kaicheng, Xulang Zhang, Rui Mao, and Erik Cambria. "Understanding the Hidden State of Language Models from the Perspective of Sentiment Analysis." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00072.

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Nagata, Fusaomi, Singo Sakata, Hirohisa Kato, Keigo Watanabe, and Maki K. Habib. "Defect Detection and Visualization of Understanding Using Fully Convolutional Data Description Models." In 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024. http://dx.doi.org/10.1109/iiai-aai63651.2024.00024.

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Rai, Teena, Yuan Shen, Jun He, et al. "Understanding Feature Importance of Prediction Models Based on Lung Cancer Primary Care Data." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650819.

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Matsuno, Ryuta, Keita Sakuma, and Masakazu Hirokawa. "Model Accuracy-Oriented Data Sets Visualization for Understanding Temporal Changes in Data." In 2024 28th International Conference Information Visualisation (IV). IEEE, 2024. http://dx.doi.org/10.1109/iv64223.2024.00041.

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P, Thilakavathy, Manikandan G, Deepa R, Jayalakshmi V, and Surendran R. "Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10864102.

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Mathur, Kartik, Nakul Mathur, and Saikat Gochhait. "Decision Models for Understanding Binge Watching Behavior Among Young Users on OTT Platforms." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836478.

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Nguyen, Thong, Yi Bin, Junbin Xiao, et al. "Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.217.

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Honavar, Vasant. "Learning predictive models from large distributed autonomous data sources." In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382178.

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Reports on the topic "Understanding of data models"

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Glass, Samuel V., Samuel L. Zelinka, Charles R. Boardman, and Emil Engelund Thybring. Promoting advances in understanding water vapor sorption in wood: relegating popular models and misconceptions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541615744.

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Water vapor sorption is a fundamental characteristic of wood as a building material. Apart from empirical prediction, models are often used to interpret the time-dependent process of water vapor uptake (sorption kinetics) and equilibrium states of water in wood (sorption isotherms). This paper summarizes our recent investigations into measurement methods and popular models that are widely used for interpreting these physical phenomena. Commonly used criteria for determining equilibrium moisture content with the dynamic vapor sorption technique yield much larger errors than previously thought.
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Weijters, Bert. Analyzing Experimental Data in Structural Equation Models. Instats Inc., 2023. http://dx.doi.org/10.61700/zclk0a8vgkfaa706.

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This two-day workshop, 'Analyzing Experimental Data using Structural Equation Modeling', led by Bert Weijters from Ghent University, provides a comprehensive understanding of SEM and its applications in research, with a focus on using Mplus software for SEM analysis. Ideal for PhD students, professors, and professional researchers in Psychology, Education, Management, and Marketing, the seminar offers practical experience in data analysis and an official Instats certificate of completion, with ECTS Equivalent points for European students.
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/pmgm4wmm7ffer469.

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This workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Gruber, Peter H. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/zqir2dzchct5b469.

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This 2-day workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis 2.0. Instats Inc., 2023. http://dx.doi.org/10.61700/txvjolg6id2hj469.

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This 2-day workshop (specifically tailored for the Australian/Asian and US West coast time zones) is aimed at non-statisticians who have to perform data analysis in their work or research. It teaches how to use ChatGPT for statistical analysis without having to know a statistics program or writing a single line of code. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its imp
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Xie, Haiyan, and Sai Ram Gandla. Data Collection and Presentation of Inclusive Pedagogy and Diversity in Engineering Education. Illinois State University, 2025. https://doi.org/10.30707/1744838575.245976.

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This project examines the impact of inclusive teaching practices and the presence of diverse role models on students' perceptions of Diversity, Equity, Belonging, and Inclusion (DEBI) in higher education, particularly among engineering and technology students. It seeks to provide a nuanced understanding of how these factors shape student experiences and perceptions of inclusivity and belonging within academic settings. This paper includes the questionnaire for the data collection and the graphical presentation of the bibliometric analysis.
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Grimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in Mplus. Instats Inc., 2024. http://dx.doi.org/10.61700/2c14h0c6ktix9661.

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The 'Factor Analysis and Measurement Invariance with Categorical Data in Mplus' seminar, led by professor Kevin Grimm from Arizona State University, offers a comprehensive understanding of confirmatory factor models for binary and ordered categorical indicators and the examination of measurement invariance, a critical concept in social sciences research. Participants will gain hands-on experience in using Mplus for analysis, learn to interpret and report results effectively, and acquire strategies to deal with non-invariance, enhancing their research skills and ensuring the robustness of their
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Grimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in R. Instats Inc., 2024. http://dx.doi.org/10.61700/6q6pcruvzduci667.

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The 'Factor Analysis and Measurement Invariance with Categorical Data in R' seminar, led by professor Kevin Grimm from Arizona State University, offers a comprehensive understanding of confirmatory factor models for binary and ordered categorical indicators and the examination of measurement invariance, a critical concept in social sciences research. Participants will gain hands-on experience in using the lavaan package in R for analysis, learn to interpret and report results effectively, and acquire strategies to deal with non-invariance, enhancing their research skills and ensuring the robus
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Altman, Safra, Krystyna Powell, and Marin Kress. Marine bioinvasion risk : review of current ecological models. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47820.

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This special report describes the first phase of developing an ecological model to inform marine bioinvasion risks in the United States. The project responds to the needs of the US Army Corps of Engineers (USACE) Aquatic Nuisance Species Research Program, or ANSRP, which addresses all problematic invasive aquatic species affecting the nation’s waterways, infrastructure, and associated resources, and the needs of the USACE navigation and dredging programs. Multiple port-deepening studies are either in progress or under consideration, and all must address ecological risk. Understanding whether a
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