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Journal articles on the topic 'Visualization – Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

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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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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Bajić, Filip, Josip Job, and Krešimir Nenadić. "Data Visualization Classification Using Simple Convolutional Neural Network Model." International journal of electrical and computer engineering systems 11, no. 1 (2020): 43–51. http://dx.doi.org/10.32985/ijeces.11.1.5.

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Data visualization is developed from the need to display a vast quantity of information more transparently. Data visualization often incorporates important information that is not listed anywhere in the document and enables the reader to discover significant data and save it in longer-term memory. On the other hand, Internet search engines have difficulty processing data visualization and connecting visualization and the request submitted by the user. With the use of data visualization, all blind individuals and individuals with impaired vision are left out. This article utilizes machine learn

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Singh,, Annu. "Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33437.

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Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data file

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Charlton, Billy, and Janek Laudan. "Web-Based Data Visualization Platform for MATSim." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (2020): 124–33. http://dx.doi.org/10.1177/0361198120935109.

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There are many tools available for analyzing MATSim transport simulation results, both open-source and commercial. This research builds a new open-source visualization platform for MATSim outputs that is entirely web-based. After initial experiments with many different web technologies, a client-server platform design emerges which leverages the advanced user interface capabilities of modern browsers on the front-end, and relies on back-end server processing for more processor-intensive tasks. The initial platform is now operational and includes several aggregate-level visualizations including

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Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (2018): 9–16. http://dx.doi.org/10.33889/ijmems.2018.3.1-002.

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Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially

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Devineni, Siva Karthik. "AI-Enhanced Data Visualization: Transforming Complex Data into Actionable Insights." Journal of Technology and Systems 6, no. 3 (2024): 52–77. http://dx.doi.org/10.47941/jts.1911.

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Purpose: The purpose of this study is to explore how artificial intelligence (AI) becomes a part of data visualization. Thus, data from complex datasets are transformed into dynamic, interactive, and personalized visual experiences that will help in deeper insights and actionable knowledge. The research is supposed to design a holistic system and rules for using AI to make data visualization more effective and super interactive for the users. Methodology: The methodology involves the in-depth examination of artificial intelligence-based data visualization tools and platforms by using case stud

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Monakhov, Vadim, Alexey Kozhedub, Nail Khannanov, Alexander Korolev, and Svetlana Kurashova. "Processing and Visualization of Test-Results Data." Computer Tools in Education, no. 5 (October 30, 2018): 24–40. http://dx.doi.org/10.32603/2071-2340-2018-5-24-40.

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Neubauer, Georg. "Visualization of typed links in Linked Data." Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare 70, no. 2 (2017): 179–99. http://dx.doi.org/10.31263/voebm.v70i2.1748.

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The main subject of the work is the visualization of typed links in Linked Data. The academic subjects relevant to the paper in general are the Semantic Web, the Web of Data and information visualization. The Semantic Web, invented by Tim Berners-Lee in 2001, was announced as an extension to the World Wide Web (Web 2.0). The actual area of investigation concerns the connectivity of information on the World Wide Web. To be able to explore such interconnections, visualizations are critical requirements as well as a major part of processing data in themselves. In the context of the Semantic Web,

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Callieri, M., P. Cignoni, F. Ganovelli, et al. "Visualization viewpoints - Visualization and 3d data processing in the David restoration." IEEE Computer Graphics and Applications 24, no. 2 (2004): 16–21. http://dx.doi.org/10.1109/mcg.2004.1274056.

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Yoo, Sangbong, Seongmin Jeong, and Yun Jang. "Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels." Sensors 21, no. 14 (2021): 4686. http://dx.doi.org/10.3390/s21144686.

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Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath vis

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Lin, Jia, Bigerng Zheng, and Zhijian Chen. "Application of Data Visualization Interaction Technology in Aerospace Data Processing." Scalable Computing: Practice and Experience 24, no. 3 (2023): 641–50. http://dx.doi.org/10.12694/scpe.v24i3.2438.

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A visualization and interactive network topology model are studied based on real-time features generated during spaceflight. Start by establishing a consistent set of data and logical interaction interfaces. This paper presents a method of scenario model construction and application programming based on virtual reality technology. The scene elements are extracted into two types of primitives, namely logical type and simulated object type. This provides a unified architecture for the editing and processing of graphic elements. This system can realize the automatic creation of the scene. Then th

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KOBAYASHI, Kenichi. "Image Processing and Data Visualization by Excel 2007." Journal of the Visualization Society of Japan 28-1, no. 2 (2008): 1047. http://dx.doi.org/10.3154/jvs.28.1047.

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James, Curtis N., Stacy R. Brodzik, Harry Edmon, Robert A. Houze, and Sandra E. Yuter. "Radar Data Processing and Visualization over Complex Terrain*." Weather and Forecasting 15, no. 3 (2000): 327–38. http://dx.doi.org/10.1175/1520-0434(2000)015<0327:rdpavo>2.0.co;2.

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Bhargava, Rohit, Shi-Qing Wang, and Jack L. Koenig. "Processing FT-IR Imaging Data for Morphology Visualization." Applied Spectroscopy 54, no. 11 (2000): 1690–706. http://dx.doi.org/10.1366/0003702001948745.

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Albert, I., S. Wachi, C. Jiang, and B. F. Pugh. "GeneTrack--a genomic data processing and visualization framework." Bioinformatics 24, no. 10 (2008): 1305–6. http://dx.doi.org/10.1093/bioinformatics/btn119.

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Kim, Sung Hwa, Jihye Lim, and Dae Ryong Kang. "Statistical Methods for Visualizing Healthcare Big Data." Journal of Health Informatics and Statistics 48, Suppl 2 (2023): S23—S33. http://dx.doi.org/10.21032/jhis.2023.48.s2.s23.

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With the advancement and acceleration of digital technology, the demand and supply of healthcare big data are increasing. In Korea, the government and companies have made various efforts to utilize healthcare big data, such as deregulation data-related legal regulations and data linkage between different institutions. As a result, many researchers have been able to access a variety of healthcare big data. Although healthcare big data has a vast amount and high value, many researchers are unable to fully access healthcare big data because there are difficulties in processing, analysis, and inte

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Li, Hongchao, and Fang Wu. "Conversion and Visualization of Remote Sensing Image Data in CAD." Computer-Aided Design and Applications 18, S3 (2020): 82–94. http://dx.doi.org/10.14733/cadaps.2021.s3.82-94.

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In this paper, a process visualization model for remote sensing image classification algorithms is constructed to analyze the current processing characteristics of process visualization in remote sensing application systems. The usability of the model is verified in a remote sensing application system with a remote sensing image classification algorithm based on support vector machines as an example. Given the characteristics of remote sensing applications that require high visualization process and a large amount of data processing, the basic process of an image classification algorithm for r

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Shu, Lei, and Dongchen Zheng. "Exploring the Application of Big Data Visualization Platform in Urban Traffic Data Analysis." Journal of Global Humanities and Social Sciences 4, no. 4 (2023): 176–80. http://dx.doi.org/10.61360/bonighss232014170805.

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With the rapid development of information technology and the popularity of the Internet, the era of big data has come. The urban traffic system continuously generates a large amount of traffic data, which are so large and diverse that the traditional data processing methods have been unable to effectively process and analyze them. Therefore, it is necessary to process and analyze urban traffic data with the help of emerging big data technologies and methods. Starting from introducing the characteristics of big data visualization platforms and common big data visualization tools and technologie

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S, Manishankar, and S. Sathayanarayana. "Enhanced Big Data Platform for Visualization of Employee Data." JOIV : International Journal on Informatics Visualization 2, no. 3 (2018): 169. http://dx.doi.org/10.30630/joiv.2.3.132.

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In this Digital world storage area capacity required for an Enterprise is quite huge, and processing that Big Data is one of the major challenging areas in today’s information technology. As the heterogeneous data from the various sources grow rapidly, there should be some proficient way for data storage for each enterprise. Most of the Enterprises have a tendency to migrate their data in to servers with high processing capability to handle variety and voluminous data. Major problem that arises in such big data servers of an Enterprise is the process involved in segregating data according to t

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Afanasyev, V. S., and S. A. Kiselev. "Modern methods of processing and visualization of meteorological data." Quality. Innovation. Education, no. 4 (2020): 61–66. http://dx.doi.org/10.31145/1999-513x-2020-4-61-66.

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The paper presents the results of processing meteorological data obtained using software that allows standard mathematical and statistical processing of model data (global and regional climate modeling, Reanalysis data), followed by a graphical representation of the results. The main tools for data processing are Panoply and GrADS, which allow you to save time when solving problems related to meteorology and climatology.

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Feng, Hui, and Guozhen Chen. "A Novel Data Visualization Model Based on Autoencoder Using Big Data Analysis and Distributed Processing Technology." Scientific Programming 2022 (January 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/7698174.

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From the standpoint of visual elements, this article investigates the use of visual information technology in visual communication design. At this time, information visualization and data visualization are widely used to display visual form, which greatly facilitates people’s use, provides a solid application foundation for visual communication design, and promotes its development. The image presentation of data is a common encoding process, and the reading of image content is the corresponding decoding process from the perspective of encoding and decoding. The combined efficacy of data encodi

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Alvarellos, Alberto, Adrián Vázquez, and Juan Rabuñal. "Raspberry Pimu: Raspberry Pi Based Inertial Sensor Data Processing System." Proceedings 2, no. 18 (2018): 1159. http://dx.doi.org/10.3390/proceedings2181159.

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This paper explains the architectural design and development of an application for the reception, visualization and storage of inertial sensor data provided by an inertial measurement system (IMU). The application is built to run in a Raspberry Pi equipped with a small size screen that allows the visualization of the data and the control of data recording. The IMU is connected to a Raspberry Pi through a serial port (USB-TTY).

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MATSUDA, Namio. "Computers as Research Tools. 2. Data Processing and Visualization." Journal of Plasma and Fusion Research 78, no. 2 (2002): 144–54. http://dx.doi.org/10.1585/jspf.78.144.

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SATO, Kaname, Mikiko ODASHIMA, and Youichi CHIBA. "Visualization Data Processing of Flow Prediction in Heat Exchanger." Journal of the Visualization Society of Japan 25, Supplement1 (2005): 347–50. http://dx.doi.org/10.3154/jvs.25.supplement1_347.

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25

Pietzsch, Tobias, Stephan Saalfeld, Stephan Preibisch, and Pavel Tomancak. "BigDataViewer: visualization and processing for large image data sets." Nature Methods 12, no. 6 (2015): 481–83. http://dx.doi.org/10.1038/nmeth.3392.

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26

Fousek, J. "13. Processing and visualization of high resolution EEG data." Clinical Neurophysiology 125, no. 5 (2014): e29. http://dx.doi.org/10.1016/j.clinph.2013.12.051.

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27

Cobas, Carlos, Isaac Iglesias, and Felipe Seoane. "NMR data visualization, processing, and analysis on mobile devices." Magnetic Resonance in Chemistry 53, no. 8 (2015): 558–64. http://dx.doi.org/10.1002/mrc.4234.

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Cobas, Carlos, Isaac Iglesias Fernández, and Felipe Seoane Otero. "NMR data visualization, processing, and analysis on mobile devices." Magnetic Resonance in Chemistry 53, no. 8 (2015): 557. http://dx.doi.org/10.1002/mrc.4300.

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29

Jin, Yuchen, Chicheng Xu, Tao Lin, Weichang Li, and Mohamed Larbi Zeghlache. "Python Dash for Well Data Validation, Visualization, and Processing." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 64, no. 4 (2023): 568–73. http://dx.doi.org/10.30632/pjv64n4-2023a6.

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Open-source Python libraries play a critical role in facilitating the digital transformation of the energy industry by enabling quick deployment of intelligent data-driven solutions. In this paper, we demonstrate an example of using Dash, a Python framework introduced by Plotly for creating interactive web applications. A fit-for-purpose software was tailored for an in-house research project in well-data validation, visualization, and processing. The application automates quality control of different sets of well-log data files (DLIS/LIS or LAS) for completeness, validity, and repeatability. F

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Weifeng Shan, Weifeng Shan, Jianqiao Li Weifeng Shan, Yuntian Teng Jianqiao Li, Huiling Chen Yuntian Teng, Zhiyang Li Huiling Chen, and Maofa Wang Zhiyang Li. "A Progressive Real-time Visualization Method for Earthquake Big Data." 電腦學刊 33, no. 1 (2022): 087–100. http://dx.doi.org/10.53106/199115992022023301009.

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<p>As the volume of seismic observation time-series data grows larger, web-based visualization schemes suffer from longer system response times. Although big data visualization schemes based on sampling and filtering can greatly reduce the data scale and shorten transmission time, what it gains in speed it loses in information. Progressive visualization has become an increasingly popular scheme because it can quickly &ldquo;see&rdquo; some results without having to wait for all the data, thus enabling users to grasp a data-change trend quickly and perceive the rules behind it. In

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Teuscher, Balthasar, and Martin Werner. "Random Data Distribution for Efficient Parallel Point Cloud Processing." AGILE: GIScience Series 5 (May 30, 2024): 1–10. http://dx.doi.org/10.5194/agile-giss-5-15-2024.

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Abstract. Current point cloud data management systems and formats are heavily specialized and targeted solely towards visualization purposes and fail to address the diverse needs of progressive point cloud workflows like for example semantic segmentation using machine learning. We therefore propose a distributed data infrastructure for dynamic point cloud data management that can support interactive real-time visualization at scale while simultaneously serving as a platform for analytical tasks. By introducing random data distribution, we show that simple query fragmentation and efficient and

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Akimkina, E. E. "Structuring and visualization of indicators in multidimensional data cubes." Informacionno-technologicheskij vestnik, no. 4 (December 30, 2018): 79–87. http://dx.doi.org/10.21499/2409-1650-2018-4-79-87.

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The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

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Gorodov, Evgeniy Yur’evich, and Vasiliy Vasil’evich Gubarev. "Analytical Review of Data Visualization Methods in Application to Big Data." Journal of Electrical and Computer Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/969458.

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This paper describes the term Big Data in aspects of data representation and visualization. There are some specific problems in Big Data visualization, so there are definitions for these problems and a set of approaches to avoid them. Also, we make a review of existing methods for data visualization in application to Big Data and taking into account the described problems. Summarizing the result, we have provided a classification of visualization methods in application to Big Data.

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Badenko, Vladimir, Dmitry Zotov, and Alexander Fedotov. "Hybrid processing of laser scanning data." E3S Web of Conferences 33 (2018): 01047. http://dx.doi.org/10.1051/e3sconf/20183301047.

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In this article the analysis of gaps in processing of raw laser scanning data and results of bridging the gaps discovered on the base of usage of laser scanning data for historic building information modeling is presented. The results of the development of a unified hybrid technology for the processing, storage, access and visualization of combined laser scanning and photography data about historical buildings are analyzed. The first result of the technology application for the historical building of St. Petersburg Polytechnic University shows reliability of the proposed approaches.

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Bornik, Alexander, and Wolfgang Neubauer. "3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data." Remote Sensing 14, no. 7 (2022): 1709. http://dx.doi.org/10.3390/rs14071709.

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The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeolo

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Joshi, Ankush, and Haripriya Tiwari. "An Overview of Python Libraries for Data Science." Journal of Engineering Technology and Applied Physics 5, no. 2 (2023): 85–90. http://dx.doi.org/10.33093/jetap.2023.5.2.10.

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In this era Python is the most popular as well as in -demand language for Data Science due to the number of libraries available for data processing, analysis and data visualization. The aim of this review paper is to give the overview of different available libraries. For this we grouped 48 different libraries in 3 different categories which are Data Collection, Data Analysis & Processing and Data Visualization. For comparison we use the GitHub community base (Stars, Forks and commits) as well as their properties and functionalities.

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Astsatryan, Hrachya, Hayk Grogoryan, Eliza Gyulgyulyan, et al. "Weather Data Visualization and Analytical Platform." Scalable Computing: Practice and Experience 19, no. 2 (2018): 79–86. http://dx.doi.org/10.12694/scpe.v19i2.1351.

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This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical c

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Bulyha, Kostiantin, Olena Bulyha, and Kateryna Kotsiubivska. "Cloud LMS of Statistic Data Visualization." Digital Platform: Information Technologies in Sociocultural Sphere 5, no. 1 (2022): 9–16. http://dx.doi.org/10.31866/2617-796x.5.1.2022.261282.

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The purpose of the article is to create a cloud LMS (Learning Management System) analysis and visualization of statistics. The research methodology is cloud information processing technologies. The novelty of the research is the implementation of a new training course on the use of modern means of data visualization in the form of cloud LMS. Conclusions. The material presented in the article gives a clear example of cloud technology usage in distance education. The materials of the training course “Information and Analytical Programs and Services” are implemented in the form of a cloud LMS bas

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Wang, Tianjun, Cengceng Wang, Jiangtao Guo, and dildar alim. "Visual Data Analysis Technology Based on Data Center." Journal of Physics: Conference Series 2146, no. 1 (2022): 012016. http://dx.doi.org/10.1088/1742-6596/2146/1/012016.

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Abstract Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data man

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Ignatov, Yuri, Oleg Tailakov, Evgeniy Saltymakov, and Daniil Gorodilov. "Development of an electrical exploration data post-processor." E3S Web of Conferences 315 (2021): 03027. http://dx.doi.org/10.1051/e3sconf/202131503027.

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In modern times, the development of geology and geophysics is associated with complex experiments. The results of these experiments are large arrays of numerical data, which require processing and further analysis. If to process these data manually, it can be a very difficult and routine task. For such studies, specialized tools are important, which are necessary to significantly speed up the processing process and to render visualization of geophysical data in real time. The software is worked out to automate the geophysical data processing obtained after electrical exploration procedure. The

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Jäger, D., and V. Gümmer. "PytonDAQ – A Python based measurement data acquisition and processing software." Journal of Physics: Conference Series 2511, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2511/1/012016.

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Abstract This paper introduces PythonDAQ, an open-source Python package for measurement data acquisition, visualization, storage, and post-processing. The code is capable of acquiring measurement data from any sensor with digital data output, performs online calculations, and stores the measured and computed data. A client for live data visualization and tools for postprocessing are also contained in the software package. First, the code is introduced explaining the software architecture and the currently implemented features. Then, the usability is demonstrated by an application at the low-sp

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Cammarano, Mike, Xin Dong, Bryan Chan, et al. "Visualization of Heterogeneous Data." IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (2007): 1200–1207. http://dx.doi.org/10.1109/tvcg.2007.70617.

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Murphy, Tara, Peter Lamb, Christopher Owen, and Malte Marquarding. "Data Storage, Processing, and Visualization for the Australia Telescope Compact Array." Publications of the Astronomical Society of Australia 23, no. 1 (2006): 25–32. http://dx.doi.org/10.1071/as05033.

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AbstractWe present three Virtual Observatory tools developed at the Australia Telescope National Facility (ATNF) for the storage, processing and visualization of Australia Telescope Compact Array (ATCA) data. These are the Australia Telescope Online Archive, a prototype data-reduction pipeline, and the Remote Visualization System. These tools were developed in the context of the Virtual Observatory and were intended to be both useful for astronomers and technology demonstrators. We discuss the design and implementation of these tools, as well as issues that should be considered when developing

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Thudt, Alice, Charles Perin, Wesley Willett, and Sheelagh Carpendale. "Subjectivity in personal storytelling with visualization." Information Design Journal 23, no. 1 (2017): 48–64. http://dx.doi.org/10.1075/idj.23.1.07thu.

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In this article we explore visualization for personal storytelling and investigate techniques for communicating subjective experiences in personal visual narratives. Personal stories are often subjective and storytellers omit, make up, or embellish details to craft engaging stories or to communicate a perspective. As growing personal data collections allow individuals to leverage visualizations, we explore how personal visual narratives can express subjectivity. From an analysis of personal visualizations created by data enthusiasts, designers and artists, we collect techniques for deliberatel

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Kroupa, Jiri, Eliska Tumova, Zdenek Tuma, Jiri Kovar, and Vladislav Singule. "PROCESSING AND VISUALIZATION OF MICROCLIMATIC DATA BY USING VIRTUAL REALITY." MM Science Journal 12, no. 2018 (2018): 2621–24. http://dx.doi.org/10.17973/mmsj.2018_12_2018104.

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Wang, Tong, Lei Zhao, Yanfeng Cao, Zhijian Qu, and Panjing Li. "Medical Data Visualization Analysis and Processing Based on Machine Learning." Journal of Computer and Communications 06, no. 11 (2018): 299–310. http://dx.doi.org/10.4236/jcc.2018.611027.

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Lesný, P., J. Vejvalka, and H. Krásnièanová. "TME17/473: Web-Based Visualization and Processing of Anthropometric Data." Journal of Medical Internet Research 1 (September 19, 1999): e124. http://dx.doi.org/10.2196/jmir.1.suppl1.e124.

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Guggenberger, Konstanze, Axel J. Krafft, Ute Ludwig, et al. "Intracranial vessel wall imaging framework – Data acquisition, processing, and visualization." Magnetic Resonance Imaging 83 (November 2021): 114–24. http://dx.doi.org/10.1016/j.mri.2021.08.004.

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Chaparro-Peláez, Julián, Santiago Iglesias-Pradas, Francisco J. Rodríguez-Sedano, and Emiliano Acquila-Natale. "Extraction, Processing and Visualization of Peer Assessment Data in Moodle." Applied Sciences 10, no. 1 (2019): 163. http://dx.doi.org/10.3390/app10010163.

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Abstract:

Situated in the intersection of two emerging trends, online self- and peer assessment modes and learning analytics, this study explores the current landscape of software applications to support peer assessment activities and their necessary requirements to complete the learning analytics cycle upon the information collected from those applications. More particularly, the study focuses on the specific case of Moodle Workshops, and proposes the design and implementation of an application, the Moodle Workshop Data EXtractor (MWDEX) to overcome the data analysis and visualization shortcomings of t

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von Landesberger, Tatiana, Dieter W. Fellner, and Roy A. Ruddle. "Visualization System Requirements for Data Processing Pipeline Design and Optimization." IEEE Transactions on Visualization and Computer Graphics 23, no. 8 (2017): 2028–41. http://dx.doi.org/10.1109/tvcg.2016.2603178.

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