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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 (June 2, 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 using Tableau. The results of the study indicate that the use of data visualization in the form of management graphics, such as horizontal bars and treemaps, is highly effective in identifying the comparison of the number of applications based on categories and ratings. These visualizations facilitate understanding the distribution and trends of applications on the Google Play Store. In conclusion, this research demonstrates that data visualization with Tableau can optimize big data processing and provide valuable insights into the distribution of app categories and ratings on the Google Play Store. These findings underscore the importance of using visualization tools in big data analysis to enhance understanding and improve decision-making.

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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 (April 15, 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 learning to classify data visualizations into 10 classes. Tested model is trained four times on the dataset which is preprocessed through four stages. Achieved accuracy of 89 % is comparable to other methods’ results. It is showed that image processing can impact results, i.e. increasing or decreasing level of details in image impacts on average classification accuracy significantly.

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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 (May 9, 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 files and receive automated insights, default visualizations, and the ability to generate custom visualizations through intuitive natural language prompts. The solution streamlines the workflow, eliminating the need for coding expertise while ensuring data privacy and integrity within a secure execution environment. User studies and benchmarking demonstrate increased productivity, time savings, and high user satisfaction. This solution has the potential to democratize data analysis and visualization, empowering decision-makers across various industries with data-driven insights and informed decision-making processes.

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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 (July 22, 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 origin–destination flows, transit supply, and emissions levels as well as a fully disaggregate traffic animation visualization. These visualizations are general enough to be useful for various projects. Further work is needed to make them more compelling and the platform more useful for practitioners.

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Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (March 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 Graphics Processing Units (GPUs) helps accelerate IT network analysis and visualization.

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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 (May 19, 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 studies. The study analyses the impact of AI technologies such as machine learning, natural language processing, and augmented and virtual reality on the scalability, interactivity, and personalization of data visualizations. The sentence also talks about the analysis of the moral factors that are part of the process of introducing AI in data visualization. Findings: The findings indicate that AI greatly improves the process and the quality of data visualization, thus, it makes possible the management of big, complicated, multi-dimensional datasets in a more efficient and precise way. The AI-driven tools give the users the opportunity to see the actions that are happening in real-time, predict the results, and personalize the tools according to their individual needs, thereby increasing the decision-making processes. Furthermore, ethical issues like data privacy, bias, and transparency must be well managed. This research has the distinctive feature of providing a theoretical framework that emphasizes the importance of AI in the development of data visualization technologies. Unique contribution to theory, policy and practice: In practice, it gives the rules for the implementation of AI tools to achieve more effective and user-focused visualizations. The policy focuses on the necessity of ethical standards in AI deployments, which means the data visualization practices should be transparent, accountable, and bias-free, thus creating trust and reliability in the AI applications.

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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 (September 12, 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, representation of information interrelations can be achieved using graphs. The aim of the article is to primarily describe the arrangement of Linked Data visualization concepts by establishing their principles in a theoretical approach. Putting design restrictions into context leads to practical guidelines. By describing the creation of two alternative visualizations of a commonly used web application representing Linked Data as network visualization, their compatibility was tested. The application-oriented part treats the design phase, its results, and future requirements of the project that can be derived from this test.

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Callieri, M., P. Cignoni, F. Ganovelli, G. Impoco, C. Montani, P. Pingi, F. Ponchio, and R. Scopigno. "Visualization viewpoints - Visualization and 3d data processing in the David restoration." IEEE Computer Graphics and Applications 24, no. 2 (March 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 (July 8, 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 visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

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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
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 multilevel visualization tool for any type of biosignals, based on subsampling is presented, focused on four representative signal parameters (mean, maximum, minimum and standard deviation error). The visualization tool enables an overview of the entire signal and a more detailed visualization in specific parts which we want to highlight, allowing an user friendly interaction that leads to an easier signal exploring. The ”map” and ”reduce” concept is also exposed for long-term biosignal processing. A processing tool (ECG peak detection) was adapted for long-term biosignals. In order to test the developed algorithm, long-term biosignals acquisitions (approximately 8 hours each) were carried out. The visualization tool has proven to be faster than the standard methods, allowing a fast navigation over the different visualization levels of biosignals. Regarding the developed processing algorithm, it detected the peaks of long-term ECG signals with fewer time consuming than the nonparalell processing algorithm. The non-specific characteristics of the new data structure, visualization tool and the speed improvement in signal processing introduced by these algorithms makes them powerful tools for long-term biosignals visualization and processing.

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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 dynamic data is a challenge for the organizations. The 2D visualization of big data has been actively in use in software industry. Though the 2D visualization has numerous advantages, this study is focused on the 3D representation of CI big data visualization and its advantage over 2D visualization. Interactivity with the data and system, and accessibility of the data anytime, anywhere are two important requirements for the system to be usable. Thus, the study focused in creating a 3D user interface to visualize CI system data in 3D web environment. The three-dimensional user interface has been studied by many researchers who have successfully identified various advantages of 3D visualization along with various interaction techniques. Researchers have also described how the system is useful in real world 3D applications. But the usability of 3D user interface in visualizations in not yet reached to a desirable level especially in software industry due its complex data. The purpose of this thesis is to explore the use of 3D data visualization that could help the CI system users of a beneficiary organization in interpreting and exploring CI system data. The study focuses on designing and creating a 3D user interface for providing a more effective and usable system for CI data exploration. Design science research framework is chosen as a suitable research method to conduct the study. This study identifies the advantages of applying 3D visualization to a software system data and then proceeds to explore how 3D visualization could help users in exploring the software data through visualization and its features. The results of the study reveal that the 3D visualization help the beneficiary organization to view and compare multiple datasets in a single screen space, and to see the holistic view of large datasets, as well as focused details of multiple datasets of various categories in a single screen space. Also, it can be said from the results that the 3D visualization help the beneficiary organization CI team to better represent big data in 3D than in 2D.

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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 create new special challenges for visualization research. This thesis presents several contributions to the field of glyph-based visualization. Glyphs are parametrised objects which encode one or more data values to its appearance (also referred to as visual channels) such as their size, colour, shape, and position. They have been widely used to convey information visually, and are especially well suited for displaying complex, multi-faceted datasets. Its major strength is the ability to depict patterns of data in the context of a spatial relationship, where multi-dimensional trends can often be perceived more easily. Our research is set in the broad scope of multi-dimensional visualization, addressing several aspects of glyph-based techniques, including visual design, perception, placement, interaction, and applications. In particular, this thesis presents a comprehensive study on one interaction technique, namely sorting, for supporting various analytical tasks. We have outlined the concepts of glyph- based sorting, identified a set of design criteria for sorting interactions, designed and prototyped a user interface for sorting multivariate glyphs, developed a visual analytics technique to support sorting, conducted an empirical study on perceptual orderability of visual channels used in glyph design, and applied glyph-based sorting to event visualization in sports applications. The content of this thesis is organised into two parts. Part I provides an overview of the basic concepts of glyph-based visualization, before describing the state-of-the-art in this field. We then present a collection of novel glyph-based approaches to address challenges created from real-world applications. These are detailed in Part II. Our first approach involves designing glyphs to depict the composition of multiple error-sensitivity fields. This work addresses the problem of single camera positioning, using both 2D and 3D methods to support camera configuration based on various constraints in the context of a real-world environment. Our second approach present glyphs to visualize actions and events "at a glance". We discuss the relative merits of using metaphoric glyphs in comparison to other types of glyph designs to the particular problem of real-time sports analysis. As a result of this research, we delivered a visualization software, MatchPad, on a tablet computer. It successfully helped coaching staff and team analysts to examine actions and events in detail whilst maintaining a clear overview of the match, and assisted in their decision making during the matches. Abstract shortened by ProQuest.

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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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 83).
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 its quantity, quality and manner of presentation to the physician at the bedside of a patient. In this thesis, we implemented a visualization system to aid ICU clinicians in collecting, processing, and displaying available ICU data. Our goals for the system are: to be able to receive large quantities of patient data from various sources, to compute complex functions over the data that are able to quantify an ICU patient's condition, to plot the data using a clean and interactive interface, and to be capable of live plot updates upon receiving new data. We made significant headway toward our goals, and we succeeded in creating a highly adaptable visualization system that future developers and users will be able to customize.
by Shruthi Narayanan.
M. Eng.

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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. New York: Springer, 2004.

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

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Şen, Zekâi. Earth Systems Data Processing and Visualization Using MATLAB. Cham: 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. New York: 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. Berlin: Springer, 1996.

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

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Ward, Matthew. Interactive data visualization: Foundations, techniques, and applications. Natick, Mass: 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. Wien: Springer-Verlag, 1997.

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R, Johnson Christopher, Rumf Martin, Scheuermann Gerik, Polthier Konrad, Hege Hans-Christian 1954-, Hoffman David, and SpringerLink (Online service), eds. Topology-Based Methods in Visualization II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Dianne, Hansford, ed. Mathematical principles for scientific computing and visualization. Wellesley, Mass: 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, 201–26. Weinheim, Germany: 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, 73–98. Cham: 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, 183–204. Berkeley, CA: 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, 325–35. Cham: 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, 274–81. Berlin, Heidelberg: 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, 99–142. Cham: 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, 496–503. Berlin, Heidelberg: 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, 89–93. Berlin, Heidelberg: 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, 279–99. Cham: 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, 3–24. Boca Raton : CRC Press, [2020]: 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, SONG JIE, BU SUNG LEE, WENTONG CAI, and LIZHE WANG. "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). Illinois: 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. Netherlands: 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), November 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), February 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.), June 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 terms including co-processing, covisualization, coviz, etc. In this paper we describe the customization of the pre-processing GUI and corresponding development of the Helios solver code-base to effectively implement in situ analysis and visualization to reduce file IO and speed up workflows for CFD analysts. We showcase how the workflow enables the wide variety of Helios users to effectively work in post-processing tools they are already familiar with as opposed to forcing them to learn new tools in order post-process in situ data extracts being produced by Helios. These data extracts include various sources of information customized to Helios, such as knowledge about the near- and off-body grids, internal surface extracts with patch information, and volumetric extracts meant for fast post-processing of data. Additionally, we demonstrate how in situ can be used by workflow automation tools to help convey information to the user that would be much more difficult when using full data dumps.

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DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), January 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 produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, April 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.], June 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 localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.

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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), June 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), December 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.), March 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 processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.

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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), October 2006. http://dx.doi.org/10.2172/895009.

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