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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 (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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8

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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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 (September 10, 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 the point cloud data obtained by sparse reconstruction of SFM is reconstructed to the Poisson surface. You get a dense, uniform grid. Experiments show that the proposed algorithm can realize the 3D reconstruction of non-cooperative objects. The spatial feature points obtained in the spatial positioning of non-cooperative objects can provide necessary technical support for its orbit positioning. The model can quickly generate new model scenario pages according to the characteristics of the task. This method changes the display mode, which can only be static or limited dynamic before. It has also improved the efficiency of space mission preparation.

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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 (June 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 (November 2000): 1690–706. http://dx.doi.org/10.1366/0003702001948745.

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15

Albert, I., S. Wachi, C. Jiang, and B. F. Pugh. "GeneTrack--a genomic data processing and visualization framework." Bioinformatics 24, no. 10 (April 3, 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 (November 30, 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 interpretation for data. The data visualization is recognized as an important tool that can solve these limitations. Using data visualization, researchers can intuitively understand complex data and receive support for decision-making. Additionally, these visualizations promote effective communication between experts in different fields and between experts and non-experts. Visualization is used in a variety of research fields and processes, including data summarization, data exploration, and evaluation and interpretation of predictive models. Various types of visualization have different meanings depending on how they are expressed. Therefore, it is important to express the meaning of visualization appropriately. This study provides representative examples of visual representations for data summarization, data exploration, and predictive model evaluation. This study aimed to improve easier access and utilization of healthcare big data by providing R code and visualization results.

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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 (October 20, 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 remote sensing applications is summarized by analyzing the basic process of existing image classification algorithms in remote sensing applications, taking into account the characteristics of process visualization. Based on the existing process of remote sensing image classification algorithm, a process visualization model is proposed. The model takes a goal-based process acts as the basic elements of the model, provides visualization functions and interfaces for human-computer interaction through a human-computer interaction selector, and uses a template knowledge base to save processing data and realize the description of customized processes. The model has little impact on the efficiency and accuracy of the support vector machine-based remote sensing image classification algorithm during the process of process visualization and customization. Finally, the application of the model to integrate business processing of earth observation can address the problem of process customization visualization for remote sensing applications to some extent.

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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 (August 29, 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 technologies, the article analyzes the scale and characteristics of urban traffic data and the limitations of traditional data analysis methods, discusses the advantages and application prospects of visualization platforms, and focuses on the four perspectives of data cleaning and pre-processing, traffic flow and congestion analysis, traffic behavior and trend analysis, traffic simulation, and planning support. The application of big data visualization platforms in urban traffic data analysis is discussed.

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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 (May 23, 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 their types. In this research, an efficient methodology is proposed which handles the segregation of data inside a server with multi valued distribution-based clustering. These clustering-based solutions provide an efficient visualization of varying data in the server and also a separate visualization of employee data too. The paper discusses about the simulation of the clustering technique with respect to an Enterprise data and visualization of file storage structure and categorization of data, also it gives a picture of performance of the Big data server.

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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 encoding and image decoding determines the effectiveness of data visualization. It is worth noting that when it comes to “encoding and decoding,” it has been established that the design mode of data visualization and visual communication is not a process of copying images but rather an external form of human thought. Then, there is the unmistakable presence of something unseen in the encoding and decoding processes. It also serves as the encoding and decoding key in the human brain. The image is as follows. From the standpoint of encoding and decoding, this article employs the data visualization self-encoder method to obtain visual data. Design pattern representation for perceptual communication can effectively support users’ rapid motion analysis during the browsing process.

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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 (September 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 (May 28, 2015): 481–83. http://dx.doi.org/10.1038/nmeth.3392.

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

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Cobas, Carlos, Isaac Iglesias, and Felipe Seoane. "NMR data visualization, processing, and analysis on mobile devices." Magnetic Resonance in Chemistry 53, no. 8 (April 29, 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 (July 22, 2015): 557. http://dx.doi.org/10.1002/mrc.4300.

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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 (August 1, 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. For this tedious and critical process, a human expert is required to perform tasks using well-log interpretation software. A typical digital log file may contain hundreds or thousands of data channels that are difficult are difficult to visualize and validate manually. Sometimes it takes multiple iterations of communication between the data provider and the data receiver to achieve a final valid deliverable copy. By utilizing open-source Python libraries, such as DLISIO (Equinor ASA, 2022) and LASIO (Inverarity, 2023), a web interface based on Plotly-Dash is developed to visualize and check all data channels automatically and then produce a compliance summary report in PDF or HTML format. The time for validating one DLIS file that has hundreds of data channels is significantly reduced. Implementation of this automated data quality control workflow demonstrates that open-source Python libraries can significantly reduce the time from development to the deployment cycle. Quick implementation of intelligent software based on Python Plotly-Dash enables customized solutions or workflows that further improve both the effectiveness and efficiency of routine data quality control processes.

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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 (February 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 “see” 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 this paper, a Cloudberry-based progressive real-time visualization schema for earthquake big data (PVSEBD) is proposed for the first time. It greatly shortens the transmission time of each data slice, improves the user interaction experience, and meets the long-term, large-scale visualization needs of earthquake consultation business. Because the correctness of average aggregation function (AVG) in progressive visualization is often not guaranteed, this paper proposes an innovative AVG translation rule solution based on the accumulability of the COUNT and SUM aggregation functions. The experimental results showed that PVSEBD automatically adjusts the amount of data returned each time according to size and has a shorter response time for each interaction compared with the solution based on the web-based visualization toolkit Portable Progressive Parallel Processing Pipeline (P5).</p> <p> </p>

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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 effective parallelism at scale are possible. At the same time, arbitrary queries in space and time can be efficiently run over the infrastructure including query semantics which returns only a random sample of the query results or preferred points based on an importance dimension calculated, for example, from a local point density information as commonly done in point cloud visualization. To cope with the unknown amount of user-specific attributes and to support even multiple ways of deciding the importance of a given point (ground point removal, coverage of space, random subset) the system is designed to support all of them transparently as multidimensional range queries backed by spatial indices.

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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 (April 1, 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 archaeological interpretation of the resulting large 2D and 3D datasets, and related data from aerial archaeology or airborne remote sensing, etc., is a time-consuming and complex process, which requires the integration of all data at hand, respective three-dimensional imagination, and a broad understanding of the archaeological problem; therefore, informative 3D visualizations supporting the exploration of complex 3D datasets and supporting the interpretative process are in great demand. This paper presents a novel integrated 3D GPR interpretation approach, centered around the flexible 3D visualization of heterogeneous data, which supports conjoint visualization of scenes composed of GPR volumes, 2D prospection imagery, and 3D interpretative models. We found that the flexible visual combination of the original 3D GPR datasets and images derived from the data applying post-processing techniques inspired by medical image analysis and seismic data processing contribute to the perceptibility of archaeologically relevant features and their respective context within a stratified volume. Moreover, such visualizations support the interpreting archaeologists in their development of a deeper understanding of the complex datasets as a starting point for and throughout the implemented interactive interpretative process.

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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 (September 15, 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, Anush Hakobyan, Aram Kocharyan, Wahi Narsisian, Vladimir Sahakyan, et al. "Weather Data Visualization and Analytical Platform." Scalable Computing: Practice and Experience 19, no. 2 (May 10, 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 capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

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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 (June 30, 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 based on the Google Classroom platform using a modern structure of material presentation.

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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 (January 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 management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

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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 designed postprocessor performs functions of data correction and geological and geophysical profile visualization. The user interface of the program provides researchers with the ability to interactively process the initial geophysical data.

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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 (May 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-speed compressor test rig FRANCC. As last step, comparisons between PythonDAQ and commercial DAQ solutions, as well as the data acquisition software of a major aero-engine manufacturer are made.

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Cammarano, Mike, Xin Dong, Bryan Chan, Jeff Klingner, Justin Talbot, Alon Halevy, and Pat Hanrahan. "Visualization of Heterogeneous Data." IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (November 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 similar systems for future telescopes.

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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 (July 20, 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 deliberately expressing subjectivity during data collection, processing, visual encoding, and presentation. Our results prompt a discussion about the role and potential of subjectivity in personal visual storytelling.

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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 (December 12, 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, Esther Raithel, Christoph Forman, Stephan Meckel, Jürgen Hennig, Thorsten A. Bley, and Patrick Vogel. "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 (December 24, 2019): 163. http://dx.doi.org/10.3390/app10010163.

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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 the Moodle Workshop module. This research paper details the architecture design, configuration, and use of the application, and proposes an initial validation of the tool based on the current peer assessment practices of a group of learning analytics experts. The results of the small-scale survey suggest that the use of software tools to support peer assessment is not so extended as it would initially seem, but also highlight the potential of MWDEX to take full advantage of Moodle Workshops.

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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 (August 1, 2017): 2028–41. http://dx.doi.org/10.1109/tvcg.2016.2603178.

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