Готові списки джерел за темами / Data Analysis and Visualization

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Data Analysis and Visualization"

Автор: Grafiati
Опубліковано: 13 лютого 2024
Оновлено: 25 липня 2025

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Статті в журналах з теми "Data Analysis and Visualization"

1

C Nair, Geethu. "Data Visualization Tools: A Comparative Analysis." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1599–602. https://doi.org/10.21275/sr241127115505.

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2

Keswani, Hrishikesh, Krishit Shah, Hritik Hassani, Moses Gadkar, and Er Manoj Kavedia. "Data Visualization and Analysis of COVID-19 Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1328–37. http://dx.doi.org/10.22214/ijraset.2022.47179.

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Анотація:
Abstract: During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which the
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3

Chin, George, Mudita Singhal, Grant Nakamura, Vidhya Gurumoorthi, and Natalie Freeman-Cadoret. "Visual Analysis of Dynamic Data Streams." Information Visualization 8, no. 3 (2009): 212–29. http://dx.doi.org/10.1057/ivs.2009.18.

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Анотація:
For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension
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4

Dessiaming, Takdir Zulhaq, Siska Anraeni, and Suwito Pomalingo. "COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION." Jurnal Teknik Informatika (Jutif) 3, no. 5 (2022): 1203–12. http://dx.doi.org/10.20884/1.jutif.2022.3.5.310.

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Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive p
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5

O'Donoghue, Seán I., Benedetta Frida Baldi, Susan J. Clark, et al. "Visualization of Biomedical Data." Annual Review of Biomedical Data Science 1, no. 1 (2018): 275–304. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013424.

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Анотація:
The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the pro
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6

Kullman, Kaur, and Don Engel. "Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity." Journal of Defence & Security Technologies 4, no. 1 (2022): 37–52. http://dx.doi.org/10.46713/jdst.004.03.

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Анотація:
Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME's understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing
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Heer, Jeffrey, and Joseph M. Hellerstein. "Data visualization and social data analysis." Proceedings of the VLDB Endowment 2, no. 2 (2009): 1656–57. http://dx.doi.org/10.14778/1687553.1687621.

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Cruz, António, Joel P. Arrais, and Penousal Machado. "Interactive and coordinated visualization approaches for biological data analysis." Briefings in Bioinformatics 20, no. 4 (2018): 1513–23. http://dx.doi.org/10.1093/bib/bby019.

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Анотація:
AbstractThe field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only repr
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Kharismatunnisaa, Fiona, and Yourdan Saputra. "Analysis of Google Play Store Apps Data Using Tableau Data Visualization Application." Journal of Applied Science, Technology & Humanities 1, no. 3 (2024): 280–85. http://dx.doi.org/10.62535/fct2yw28.

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Анотація:
This research aims to enhance understanding of big data management and processing. One of the challenges faced is the complexity and large volume of data, which requires effective tools and techniques for analysis and visualization. The objective of this study is to analyze Google Play Store app data based on categories and ratings, and to visualize the results using Tableau. The research method employs a quantitative approach with a framework that includes problem formulation, data collection from the Google Play Store Apps database obtained from kaggle.com, data processing, and analysis usin
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Vineet, Patel, Kumar Maurya Rajan, Sharma Pooja, and Aishwarya. "Tools and Techniques of Data Visualization." Journal of Advancement in Parallel Computing 6, no. 2 (2023): 20–40. https://doi.org/10.5281/zenodo.7928983.

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Анотація:
<em>Data visualization is the process of displaying data in a graphical or pictorial style to make it easier to understand. It aids in establishing facts and deciding on courses of action. It will help any field of study that needs creative ways to convey substantial amounts of complicated material. Large datasets may be shown graphically using data visualization, a powerful tool. Several methods and technologies for data visualization are in use. </em><em>Data Presentation and Data Exploration are two major objectives of data visualization technologies. Many decision-makers rely on data visua
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Дисертації з теми "Data Analysis and Visualization"

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Furuhashi, Takeshi. "Data Visualization for Kansei Analysis." 日本知能情報ファジィ学会, 2010. http://hdl.handle.net/2237/20694.

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SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan
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Cheong, Tat Man. "Money laundering data analysis and visualization." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492978.

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Yang, Di. "Analysis guided visual exploration of multivariate data." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050407-005925/.

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Huang, Yunshui Charles. "A prototype of data analysis visualization tool." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12125.

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Wu, Yingyu. "Using Text based Visualization in Data Analysis." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1398079502.

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Alam, Sayeed Safayet. "Analysis of Eye-Tracking Data in Visualization and Data Space." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3473.

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Анотація:
Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument
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Song, Huaguang. "Multi-scale data sketching for large data analysis and visualization." Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/832.

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Анотація:
Analysis and visualization of large data sets is time consuming and sometimes can be a very difficult process, especially for 3D data sets. Therefore, data processing and visualization techniques have often been used in the case of different massive data analysis for efficiency and accuracy purposes. This thesis presents a multi-scale data sketching solution, specifically for large 3D scientific data with a goal to support collaborative data management, analysis and visualization. The idea is to allow users to quickly identify interesting regions and observe significant patterns without direct
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8

Park, Joonam. "A visualization system for nonlinear frame analysis." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/19172.

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Schroeder, Michael Philipp 1986. "Analysis and visualization of multidimensional cancer genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/301436.

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Анотація:
Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope I present two methods that exploit the broad availability of cancer genomic data: OncodriveROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical m
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Walter, Martin Alan. "Visualization techniques for the analysis of neurophysiological data." Thesis, University of Plymouth, 2004. http://hdl.handle.net/10026.1/2551.

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In order to understand the diverse and complex functions of the Human brain, the temporal relationships of vast quantities of multi-dimensional spike train data must be analysed. A number of statistical methods already exist to analyse these relationships. However, as a result of expansions in recording capability hundreds of spike trains must now be analysed simultaneously. In addition to the requirements for new statistical analysis methods, the need for more efficient data representation is paramount. The computer science field of Information Visualization is specifically aimed at producing
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Книги з теми "Data Analysis and Visualization"

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1957-, Blasius Jörg, and Greenacre Michael J, eds. Visualization of categorical data. Academic Press, 1998.

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Engebretsen, Martin. Data Visualization in Society. Amsterdam university Press, 2020.

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3

Tierny, Julien. Topological Data Analysis for Scientific Visualization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71507-0.

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Embarak, Dr Ossama. Data Analysis and Visualization Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4109-7.

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McCormick, Keith, and Jesus Salcedo. SPSSreg Statistics for Data Analysis and Visualization. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119183426.

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Pascucci, Valerio, Xavier Tricoche, Hans Hagen, and Julien Tierny, eds. Topological Methods in Data Analysis and Visualization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15014-2.

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Mirkin, Boris. Core Data Analysis: Summarization, Correlation, and Visualization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00271-8.

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Nieuwstadt, F. T. M. 1946-, ed. Flow visualization and image analysis. Kluwer Academic Publishers, 1993.

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9

Hotz, Ingrid, Talha Bin Masood, Filip Sadlo, and Julien Tierny, eds. Topological Methods in Data Analysis and Visualization VI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83500-2.

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Carr, Hamish, Christoph Garth, and Tino Weinkauf, eds. Topological Methods in Data Analysis and Visualization IV. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44684-4.

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Частини книг з теми "Data Analysis and Visualization"

1

Fries, Karsten, Jörg Meyer, Hans Hagen, and Bernd Lindemann. "Correspondence Analysis." In Data Visualization. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_4.

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Eick, Stephen G. "Ebusiness Click Stream Analysis." In Data Visualization. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_13.

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Marron, J. S., and Ian L. Dryden. "Data Visualization." In Object Oriented Data Analysis. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351189675-6.

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Embarak, Ossama. "Data Visualization." In Data Analysis and Visualization Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4109-7_7.

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Kilburn, H. Whitt. "Data Visualization." In Introduction to Political Analysis in R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003431558-4.

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Musa, Kamarul Imran, Wan Nor Arifin Wan Mansor, and Tengku Muhammad Hanis. "Data Visualization." In Data Analysis in Medicine and Health using R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003296775-4.

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Divya Zion, G., and B. K. Tripathy. "Comparative Analysis of Tools for Big Data Visualization and Challenges." In Data Visualization. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_3.

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Bennett, Janine, Attila Gyulassy, Valerio Pascucci, and Peer-Timo Bremer. "Large Scale Data Analysis." In Mathematics and Visualization. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6497-5_27.

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Umadevi, K. S., and D. Geraldine Bessie Amali. "Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform." In Data Visualization. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_2.

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Chellam, Aditya, Ayush Chaturvedi, and L. Ramanathan. "Data Visualization: Visualization of Social Media Marketing Analysis Data to Generate Effective Business Revenue Model." In Data Visualization. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_5.

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Тези доповідей конференцій з теми "Data Analysis and Visualization"

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Hasan, Khondoker Md Sabit, Shuva Saha, Mirza Sadman Mehrab, Nipa, Tohedul Islam, and Abdus Salam. "Data Analysis Using Visualization Techniques." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013513.

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Ma, Guanqun, David Lenz, Tom Peterka, Hanqi Guo, and Bei Wang. "Critical Point Extraction from Multivariate Functional Approximation." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00006.

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Rasheed, Farhan, Abrar Naseer, Emma Nilsson, Talha Bin Masood, and Ingrid Hotz. "Multi-scale Cycle Tracking in Dynamic Planar Graphs." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00009.

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Qian, Yuehui, Guoxi Liu, Federico Iuricich, and Leila De Floriani. "Efficient representation and analysis for a large tetrahedral mesh using Apache Spark." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00005.

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Lin, Xinwei, Yue Zhang, and Eugene Zhang. "Asymptotic Topology of 3D Linear Symmetric Tensor Fields." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00010.

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Thanh, Son Le, Michael Ankele, and Tino Weinkauf. "Revisiting Accurate Geometry for Morse-Smale Complexes." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00008.

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Raith, Felix, Gerik Scheuermann, and Christian Heine. "Topological Simplifcation of Jacobi Sets for Piecewise-Linear Bivariate 2D Scalar Fields with Adjustment of the Underlying Data." In 2024 IEEE Topological Data Analysis and Visualization (TopoInVis). IEEE, 2024. http://dx.doi.org/10.1109/topoinvis64104.2024.00007.

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Shahare, Gayatri, Janvi Manchalwar, Khushi Wankhede, Shreya Meshram, Kamlesh Kalbande, and Nikhil Wyawahare. "Exploring Multidisciplinary Data Visualization and Data Analysis with R." In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC). IEEE, 2024. https://doi.org/10.1109/akgec62572.2024.10869196.

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Ferro, Daniela P., Ugo Becciani, Vincenzo Antonuccio-Delogu, Angela Germaná, and Claudio Gheller. "Astrophysical Data Analysis and Visualization Toolkit." In ADA-III - Astronomical Data Analysis III Conference. BCS Learning & Development, 2004. http://dx.doi.org/10.14236/ewic/ada-iii2004.9.

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Li, Mingzhe, Sourabh Palande, Lin Yan, and Bei Wang. "Sketching Merge Trees for Scientific Visualization." In 2023 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2023. http://dx.doi.org/10.1109/topoinvis60193.2023.00013.

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Звіти організацій з теми "Data Analysis and Visualization"

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Shen, Han-Wei. Scalable Data Management, Analysis, and Visualization. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1406107.

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Astrom, Richard L. Data Analysis Tools for Visualization Study. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625734.

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Ismay, Chester. Exploratory Data Analysis in R with Tidyverse. Instats Inc., 2025. https://doi.org/10.61700/9ig8c167iq5b91556.

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Анотація:
This workshop provides a comprehensive introduction to Exploratory Data Analysis (EDA) using R and the tidyverse suite, focusing on practical data manipulation and visualization techniques essential for robust research. Participants will gain expertise in handling complex datasets and creating publication-ready visualizations, enabling them to transform raw data into actionable insights. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers ECTS Equivalent points.
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4

Choudhary, Alok. Scalable Data Management, Analysis, and Visualization Institute. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1567859.

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Ahrens, James P. Visualization and Data Analysis at the Exascale. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1011053.

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Zhan, Peng. Statistical Analysis and Data Visualization in R. Instats Inc., 2022. http://dx.doi.org/10.61700/dizyg5iq1mqj5469.

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Анотація:
This seminar provides a gentle introduction to R for those looking to use it for applied statistics and visualisation, covering a range of essential topics on basic data manipulation, regression analysis, and visualizing data and results. Examples from multiple fields are used to demonstrate the applications. An official Instats certificate of completion is provided at the conclusion of the seminar.
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7

Ma, Kwan-Liu. Scalable Data Management, Analysis, and Visualization (SDAV) Institute. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1498620.

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Bethel, E. Wes. Query-Driven Network Flow Data Analysis and Visualization. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/888963.

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9

Ma, Kwan-Liu. Interactive Correlation Analysis and Visualization of Climate Data. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1325752.

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10

Sewell, Christopher Meyer. Visualization and Data Analysis for High-Performance Computing. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1329547.

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