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Relevant bibliographies by topics / Visualization – Data processing

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Academic literature on the topic 'Visualization – Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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Journal articles on the topic "Visualization – Data processing"

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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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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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Dissertations / Theses on the topic "Visualization – Data processing"

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Huang, Shiping. "Exploratory visualization of data with variable quality." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01115-225546/.

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Gomes, Ricardo Rafael Baptista. "Long-term biosignals visualization and processing." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/7979.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering<br>Long-term biosignals acquisitions are an important source of information about the patients’state and its evolution. However, long-term biosignals monitoring involves managing extremely large datasets, which makes signal visualization and processing a complex task. To overcome these problems, a new data structure to manage long-term biosignals was developed. Based on this new data structure, dedicated tools for long-term biosignals visualization and processing were implemented. A mu

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Cai, Bo. "Scattered Data Visualization Using GPU." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428077896.

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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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Mattasantharam, R. (Rubini). "3D web visualization of continuous integration big data." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201812063239.

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Continuous Integration (CI) is a practice that is used to automate the software build and its test for every code integration to a shared repository. CI runs thousands of test scripts every day in a software organization. Every test produces data which can be test results logs such as errors, warnings, performance measurements and build metrics. This data volume tends to grow at unprecedented rates for the builds that are produced in the Continuous Integration (CI) system. The amount of the integrated test results data in CI grows over time. Visualizing and manipulating the real time and dynam

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Chung, David H. S. "High-dimensional glyph-based visualization and interactive techniques." Thesis, Swansea University, 2014. https://cronfa.swan.ac.uk/Record/cronfa42276.

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The advancement of modern technology and scientific measurements has led to datasets growing in both size and complexity, exposing the need for more efficient and effective ways of visualizing and analysing data. Despite the amount of progress in visualization methods, high-dimensional data still poses a number of significant challenges in terms of the technical ability of realising such a mapping, and how accurate they are actually interpreted. The different data sources and characteristics which arise from a wide range of scientific domains as well as specific design requirements constantly

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Peng, Wei. "Clutter-based dimension reordering in multi-dimensional data visualization." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01115-222940.

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Narayanan, Shruthi (Shruthi P. ). "Real-time processing and visualization of intensive care unit data." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/119537.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 83).<br>Intensive care unit (ICU) patients undergo detailed monitoring so that copious information regarding their condition is available to support clinical decision-making. Full utilization of the data depends heavily on it

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Wad, Charudatta V. "QoS : quality driven data abstraction for large databases." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-020508-151213/.

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Antle, Alissa N. "Interactive visualization tools for spatial data & metadata." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0010/NQ56495.pdf.

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Books on the topic "Visualization – Data processing"

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G, Brunnett, ed. Geometric modelling for scientific visualization. Springer, 2004.

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Grave, Michel. Visualization in Scientific Computing. Springer Berlin Heidelberg, 1994.

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Şen, Zekâi. Earth Systems Data Processing and Visualization Using MATLAB. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-01542-8.

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-P, Tsai Jeffrey J., ed. Distributed real-time systems: Monitoring, visualization, debugging, and analysis. Wiley, 1996.

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1937-, Hehl F. W., Puntigam R. A. 1967-, and Ruder Hanns, eds. Relativity and scientific computing: Computer algebra, numerics, visualization. Springer, 1996.

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A, Pickover Clifford, and Tewksbury Stuart K, eds. Frontiers of scientific visualization. Wiley, 1994.

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Ward, Matthew. Interactive data visualization: Foundations, techniques, and applications. A K Peters, 2010.

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Eurographics Workshop (8th 1997 Boulogne-sur-Mer,France). Visualization in scientific computing '97: Proceedings of the Eurographics Workshop in Boulogne-sur-Mer, France, April 28-30, 1997. Springer-Verlag, 1997.

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R, Johnson Christopher, Rumf Martin, Scheuermann Gerik, et al., eds. Topology-Based Methods in Visualization II. Springer Berlin Heidelberg, 2009.

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Dianne, Hansford, ed. Mathematical principles for scientific computing and visualization. AK Peters, 2008.

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Book chapters on the topic "Visualization – Data processing"

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Blanche, Pierre-Alexandre. "Holographic Visualization of 3D Data." In Optical and Digital Image Processing. Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527635245.ch10.

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Pajarola, Renato, Susanne K. Suter, Rafael Ballester-Ripoll, and Haiyan Yang. "Tensor Approximation for Multidimensional and Multivariate Data." In Mathematics and Visualization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_4.

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AbstractTensor decomposition methods and multilinear algebra are powerful tools to cope with challenges around multidimensional and multivariate data in computer graphics, image processing and data visualization, in particular with respect to compact representation and processing of increasingly large-scale data sets. Initially proposed as an extension of the concept of matrix rank for 3 and more dimensions, tensor decomposition methods have found applications in a remarkably wide range of disciplines. We briefly review the main concepts of tensor decompositions and their application to multidimensional visual data. Furthermore, we will include a first outlook on porting these techniques to multivariate data such as vector and tensor fields.

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Embarak, Ossama. "File I/O Processing and Regular Expressions." In Data Analysis and Visualization Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4109-7_4.

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Baumeister, Jan, Bernd Finkbeiner, Stefan Gumhold, and Malte Schledjewski. "Real-Time Visualization of Stream-Based Monitoring Data." In Runtime Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17196-3_21.

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AbstractStream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.

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Teh, Chee Siong, Ming Leong Yii, Chwen Jen Chen, and Zahan Tapan Sarwar. "A Hybrid Visualization-Induced Self-Organizing Map for Multi Dimensional Reduction and Data Visualization." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34481-7_34.

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Heinzl, Christoph, Alexander Amirkhanov, and Johann Kastner. "Processing, Analysis and Visualization of CT Data." In Industrial X-Ray Computed Tomography. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59573-3_4.

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Wang, Ying, and Masahiro Takatuska. "Enhancing SOM Based Visualization Methods for Better Data Navigation." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_62.

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Jeffery, Clinton L., Sandra G. Dykes, Xiaodong Zhang, Guillermo H. Gonzalez, and Jason L. Peacock. "Nova visualization for optimization of data-parallel programs." In Euro-Par'97 Parallel Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0002720.

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Seredin, Oleg, Egor Surkov, Andrei Kopylov, and Sergey Dvoenko. "Multidimensional Data Visualization Based on the Shortest Unclosed Path Search." In Artificial Intelligence in Data and Big Data Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_23.

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Vasavi, S., P. Vamsi Krishna, and Anu A. Gokhale. "Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark." In Data Science. CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-1.

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Conference papers on the topic "Visualization – Data processing"

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Berlin, Mark S. "Four-dimensional ATR processing and visualization." In 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, edited by William R. Oliver. SPIE, 2000. http://dx.doi.org/10.1117/12.384863.

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Qunchao Fu, Wanheng Liu, Tengfei Xue, Heng Gu, Siyue Zhang, and Cong Wang. "A big data processing methods for visualization." In 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2014. http://dx.doi.org/10.1109/ccis.2014.7175800.

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DATE, SUSUMU, SHIMOJO SHINJI, MIZUNO-MATSUMOTO YUKO, et al. "Distributed processing and visualization of MEG data." In Proceedings of the International Conference on Scientific and Engineering Computation (IC-SEC) 2002. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949524_0196.

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Hay, Stewart, Carl Hughes, and Peter Taylor. "Cyclone -- Monte Carlo Data Processing and Visualization." In Nuclear Criticality Safety Division Topical Meeting (NCSD 2022). American Nuclear Society, 2022. http://dx.doi.org/10.13182/t126-37914.

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Pinte, Didrik, Eric Jones, Robert Kern, and Pietro Berkes. "Python for Geophysical Data Processing and Visualization." In 74th EAGE Conference and Exhibition - Workshops. EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20149884.

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Jorgensen, Mackenzie, Jonathan Spohn, Christopher Bunn, Shi Dong, Xiangyu Li, and David Kaeli. "An interactive big data processing/visualization framework." In 2017 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2017. http://dx.doi.org/10.1109/urtc.2017.8284188.

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Feuquay, Jay W. "Data visualization techniques for hyperdimensional data." In Recent Advances in Sensors, Radiometric Calibration, and Processing of Remotely Sensed Data. SPIE, 1993. http://dx.doi.org/10.1117/12.161568.

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Huang, Xiaoman, and Bo Zhao. "SVG-based remote sensing image visualization and processing." In Geoinformatics 2006: Remotely Sensed Data and Information, edited by Liangpei Zhang and Xiaoling Chen. SPIE, 2006. http://dx.doi.org/10.1117/12.713261.

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Tremeau, Alain, and Philippe Colantoni. "Color data visualization for color imaging." In Visual Communications and Image Processing 2003, edited by Touradj Ebrahimi and Thomas Sikora. SPIE, 2003. http://dx.doi.org/10.1117/12.501823.

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Bamber, Jeffery C., R. J. Eckersley, P. Hubregtse, N. L. Bush, D. S. Bell, and Diane C. Crawford. "Data processing for 3-D ultrasound visualization of tumor anatomy and blood flow." In Visualization in Biomedical Computing, edited by Richard A. Robb. SPIE, 1992. http://dx.doi.org/10.1117/12.131117.

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Reports on the topic "Visualization – Data processing"

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Moreland, Kenneth, and Berk Geveci. A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1164814.

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Ma, Kwan-Liu. A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1341896.

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Bauer, Andrew, James Forsythe, Jayanarayanan Sitaraman, Andrew Wissink, Buvana Jayaraman, and Robert Haehnel. In situ analysis and visualization to enable better workflows with CREATE-AV™ Helios. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40846.

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The CREATE-AV™ Helios CFD simulation code has been used to accurately predict rotorcraft performance under a variety of flight conditions. The Helios package contains a suite of tools that contain almost the entire set of functionality needed for a variety of workflows. These workflows include tools customized to properly specify many in situ analysis and visualization capabilities appropriate for rotorcraft analysis. In situ is the process of computing analysis and visualization information during a simulation run before data is saved to disk. In situ has been referred to with a variety of te

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DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43042.

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In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produ

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, 2023. http://dx.doi.org/10.56227/23.1.10.

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Expert contributors discuss AI and big data applications that are being developed for financial services, such as AI-powered intelligent customer service systems; “factories” for data processing, AI, simulation, and visualization; and symbolic AI.

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Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian

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Powers, Michael H. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, & Visualization ... Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/838446.

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Wright, David L. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, & Visualization. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/850393.

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Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40042.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and pro

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David Wright, Michael Powers, Charles Oden, and Craig Moulton. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, and Visualization methods with Applications to Site Characterization. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/895009.

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