Journal articles: 'Online data visualization'

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Miller, Charles, Lucas Lecheler, Bradford Hosack, Aaron Doering, and Simon Hooper. "Orchestrating Data, Design, and Narrative." International Journal of Cyber Behavior, Psychology and Learning 2, no. 2 (April 2012): 1–15. http://dx.doi.org/10.4018/ijcbpl.2012040101.

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Information visualization involves the visual, and sometimes interactive, presentation and organization of complex data in a clear, compelling representation. Information visualization is an essential element in peoples’ daily lives, especially those in data-driven professions, namely online educators. Although information visualization research and methods are prevalent in the diverse fields of healthcare, statistics, economics, information technology, computer science, and politics, few examples of successful information visualization design or integration exist in online learning. The authors provide a background of information visualization in education, explore a set of potential roles for information visualization in the future design and integration of online learning environments, provide examples of contemporary interactive visualizations in education, and discuss opportunities to move forward with design and research in this emerging area.

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Atzl, Caroline, Bernhard Vockner, and Manfred Mittlboeck. "Online Visualization of Streaming Data." GI_Forum 2 (2016): 57–66. http://dx.doi.org/10.1553/giscience2016_02_s57.

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Costagliola, G., V. Fuccella, M. Giordano, and G. Polese. "Monitoring Online Tests through Data Visualization." IEEE Transactions on Knowledge and Data Engineering 21, no. 6 (June 2009): 773–84. http://dx.doi.org/10.1109/tkde.2008.133.

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Shamim, Azra, Vimala Balakrishnan, and Muhammad Tahir. "Evaluation of opinion visualization techniques." Information Visualization 14, no. 4 (September 24, 2014): 339–58. http://dx.doi.org/10.1177/1473871614550537.

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In this article, we are reporting the findings of a usability study of opinion mining systems’ visualizations. The objectives of this study are to first to rank the visualizations of the opinion mining systems and second to identify important visualization metrics. A questionnaire survey was designed to ask users their level of agreement or disagreement about the 11 selected visualizations against a set of information visualization metrics on a Likert scale. The data were collected by conducting seminars and using a web-based online questionnaire (N = 146). The collected data were analyzed using descriptive statistics and an independent sample t-test to rank the visualizations and investigate differences between perceptions of the two groups of respondents (the participants of the seminars and the online questionnaire), respectively. The results revealed that simple, eye pleasing, easy to understand, user-friendly visualizations with less pre-knowledge required rated higher than others. It is concluded that the participants of the online questionnaire mostly required more pre-knowledge to comprehend the visualizations as compared to the participants of the seminars. The important information visualization metrics are eye pleasing, easy to understand, user-friendly, informative design, usefulness, and representation style. The results of this study could aid in the design and development of visualizations for opinion mining systems.

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André, Tiago Silva, and Elizabeth Simão Carvalho. "Hotels Online Opinion Visualization." International Journal of Creative Interfaces and Computer Graphics 10, no. 1 (January 2019): 30–42. http://dx.doi.org/10.4018/ijcicg.2019010103.

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Nowadays there are several websites where a traveller can find information about hotels. These websites give an idea about the quality of the hotel in several aspects based on customer opinion. Although helpful, these websites do not offer visualizations in order to actually give a clear insight on available data. It does not really support travellers' decision because the information is generally showed as a text list, with or without some monochromatic symbol, not allowing any kind of interaction or taking into account relevant cognitive aspects. This article proposes a new visual interface for the booking and hotel sector, considering the customers' online opinion as its main input data.

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Walsh, Kieran, Mircea A. Voineagu, Fatemeh Vafaee, and Irina Voineagu. "TDAview: an online visualization tool for topological data analysis." Bioinformatics 36, no. 18 (July 2, 2020): 4805–9. http://dx.doi.org/10.1093/bioinformatics/btaa600.

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Abstract Summary TDAview is an online tool for topological data analysis (TDA) and visualization. It implements the Mapper algorithm for TDA and provides extensive graph visualization options. TDAview is a user-friendly tool that allows biologists and clinicians without programming knowledge to harness the power of TDA. TDAview supports an analysis and visualization mode in which a Mapper graph is constructed based on user-specified parameters, followed by graph visualization. It can also be used in a visualization only mode in which TDAview is used for visualizing the data properties of a Mapper graph generated using other open-source software. The graph visualization options allow data exploration by graphical display of metadata variable values for nodes and edges, as well as the generation of publishable figures. TDAview can handle large datasets, with tens of thousands of data points, and thus has a wide range of applications for high-dimensional data, including the construction of topology-based gene co-expression networks. Availability and implementation TDAview is a free online tool available at https://voineagulab.github.io/TDAview/. The source code, usage documentation and example data are available at TDAview GitHub repository: https://github.com/Voineagulab/TDAview.

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Estévez, Pablo A., and Cristián J. Figueroa. "Online data visualization using the neural gas network." Neural Networks 19, no. 6-7 (July 2006): 923–34. http://dx.doi.org/10.1016/j.neunet.2006.05.024.

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Archambault, Susan Gardner, Joanne Helouvry, Bonnie Strohl, and Ginger Williams. "Data visualization as a communication tool." Library Hi Tech News 32, no. 2 (April 7, 2015): 1–9. http://dx.doi.org/10.1108/lhtn-10-2014-0098.

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Purpose – This paper aims to provide a framework for thinking about meaningful data visualization in ways that can be applied to routine statistics collected by libraries. Design/methodology/approach – An overview of common data display methods is provided, with an emphasis on tables, scatter plots, line charts, bar charts, histograms, pie charts and infographics. Research on “best practices” in data visualization design is presented; also provided is a comparison of free online data visualization tools. Findings – Different data display methods are best suited for different quantitative relationships. There are rules to follow for optimal data visualization design. Ten free online data visualization tools are recommended by the authors. Originality/value – Evidence-based libraries collect and use data to affect change and to support departmental and institutional accreditation standards. Proper data visualization allows libraries to communicate their message in a more compelling and interesting way, while assisting in the understanding of complex data.

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Hannah, Matthew N. "A Conspiracy of Data: QAnon, Social Media, and Information Visualization." Social Media + Society 7, no. 3 (July 2021): 205630512110360. http://dx.doi.org/10.1177/20563051211036064.

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Seeing is believing, so goes the cliché. In our extremely online world, the particular nexus between visual information and political belief has become one of the thorniest challenges to truth. We live in an extremely visual world in which we navigate social media, search engines, platforms, interfaces, icons, memes, and smartphones. Despite the fact that we navigate visual information at an astounding rate, we have not nationally developed literacies to debunk bad information. I argue that we are witnessing a confluence between extremely online, crowd-sourced conspiracies, whose adherents possess a high capacity for online information gathering, and visualization, meant to communicate data about our world effectively and accurately through optical means which has been co-opted for information warfare. Deploying such informatics further legitimates bizarre, unhinged theories about political reality. QAnon, the extremely online conspiracy theory that has cast its shadow over the Internet, relies exclusively on information visualization to communicate its message and is symptomatic of our inability to combat misinformation that mimics the methods of data analysis and information literacy. I argue that QAnon’s success—indeed, its very existence—relies on (at least) two principal factors: (1) QAnon relies, intentionally or no, on a slippage between data and information that obscures the interventions by Q and Q’s anons in leveraging information warfare, and (2) QAnon supports such a slippage with complex and interactive visualizations of bad information, thereby accelerating apophenia, the tendency to see linkages between random events and data points.

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Fantham, Marcus, and Clemens F. Kaminski. "A new online tool for visualization of volumetric data." Nature Photonics 11, no. 2 (February 2017): 69. http://dx.doi.org/10.1038/nphoton.2016.273.

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Procopio, Marianne, Carlos Scheidegger, Eugene Wu, and Remco Chang. "Selective Wander Join: Fast Progressive Visualizations for Data Joins." Informatics 6, no. 1 (March 25, 2019): 14. http://dx.doi.org/10.3390/informatics6010014.

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Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.

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Börner, Katy, Andreas Bueckle, and Michael Ginda. "Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments." Proceedings of the National Academy of Sciences 116, no. 6 (February 4, 2019): 1857–64. http://dx.doi.org/10.1073/pnas.1807180116.

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In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical and procedural knowledge and showcases how both can be combined to design curricula and assessment measures for DVL. Earlier versions of the DVL-FW have been used to teach DVL to more than 8,500 residential and online students, and results from this effort have helped revise and validate the DVL-FW presented here.

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Perkel, Jeffrey M. "Data visualization tools drive interactivity and reproducibility in online publishing." Nature 554, no. 7690 (January 30, 2018): 133–34. http://dx.doi.org/10.1038/d41586-018-01322-9.

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Ali, Mohamed, Badrish Chandramouli, Jonathan Fay, Curtis Wong, Steven Drucker, and Balan Sethu Raman. "Online visualization of geospatial stream data using the worldwide telescope." Proceedings of the VLDB Endowment 4, no. 12 (August 2011): 1379–82. http://dx.doi.org/10.14778/3402755.3402775.

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Herring, Jamie, Matthew S. VanDyke, R. Glenn Cummins, and Forrest Melton. "Communicating Local Climate Risks Online Through an Interactive Data Visualization." Environmental Communication 11, no. 1 (May 11, 2016): 90–105. http://dx.doi.org/10.1080/17524032.2016.1176946.

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Islam, Md Shoriful, and G. M. Faruk Ahmed. "Online Analytical Processing for the Application of Data Cubes in Business Data Visualization." International Journal of Computer Graphics 7, no. 2 (November 30, 2016): 1–12. http://dx.doi.org/10.14257/ijcg.2016.7.2.01.

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Mao, B., Z. Wu, and J. Cao. "A FRAMEWORK FOR ONLINE SPATIO-TEMPORAL DATA VISUALIZATION BASED ON HTML5." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B2 (July 27, 2012): 123–27. http://dx.doi.org/10.5194/isprsarchives-xxxix-b2-123-2012.

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Jing Xia, Feiran Wu, Fangzhou Guo, Cong Xie, Zhen Liu, and Wei Chen. "An online visualization system for streaming log data of computing clusters." Tsinghua Science and Technology 18, no. 2 (April 2013): 196–205. http://dx.doi.org/10.1109/tst.2013.6509102.

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Liu, Zhong, Dana Ostrenga, and Gregory Leptoukh. "Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data." Bulletin of the American Meteorological Society 92, no. 4 (April 1, 2011): 429–32. http://dx.doi.org/10.1175/2010bams2976.1.

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Jaroensutasinee, Krisanadej, Wittaya Pheera, and Mullica Jaroensutasinee. "Online weather data analysis and visualization tools for applications in ecoinformatics." Earth Science Informatics 7, no. 3 (October 13, 2013): 205–13. http://dx.doi.org/10.1007/s12145-013-0138-y.

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Braun, Peter, Alfredo Cuzzocrea, Timothy D. Keding, Carson K. Leung, Adam G. M. Padzor, and Dell Sayson. "Game Data Mining: Clustering and Visualization of Online Game Data in Cyber-Physical Worlds." Procedia Computer Science 112 (2017): 2259–68. http://dx.doi.org/10.1016/j.procs.2017.08.141.

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Usmani, Sheema, Mariana Bernagozzi, Yufeng Huang, Michelle Morales, Amir Sabet Sarvestani, and Biplav Srivastava. "Data-Driven Ranking and Visualization of Products by Competitiveness." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13640–41. http://dx.doi.org/10.1609/aaai.v34i09.7107.

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Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through a huge volume of online and offline content, aiming to discover what their competitors are doing in the marketplace and to understand what type of threat they pose to their business' financial well-being. Currently, this process is slow, costly and labor-intensive. We demonstrate Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment at IBM. Clarity has been running for more than a year and is used by over 1,500 people to perform over 160 competitive analyses involving over 800 products. The system considers multiple factors from a collection of online content: numeric ratings by users, sentiment towards key product drivers, content volume, and recency of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track the performance of their products as well as understand the main contributing factors. main contributing factors.

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Samy, V. Sakthivel, Koyel Pramanick, Veena Thenkanidiyoor, and Jeni Victor. "Data Analysis and Visualization in Python for Polar Meteorological Data." International Journal of Data Analytics 2, no. 1 (January 2021): 32–60. http://dx.doi.org/10.4018/ijda.2021010102.

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The aim of this study is to analyze meteorological data obtained from the various expeditions made to the Indian stations in Antarctica over recent years and determine how significantly the weather has shown a marked change over the years. For any time series data analysis, there are two main goals: (a) the authors need to identify the nature of the phenomenon from the sequence of observations and (b) predict the future data. On account of these goals, the pattern in the time series data and its variability are to be accurately identified. This paper can then interpret and integrate the pattern established with its associated meteorological datasets collected in Antarctica. Using the data analytics knowledge the validity of interpretation for the given datasets a pattern has been identified, which could extrapolate the pattern towards prediction. To ease the time series data analysis, the authors developed online meteorological data analytic portal at NCPOR, Goa http://data.ncaor.gov.in/.

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Boutsi, A. M., C. Ioannidis, and S. Soile. "INTERACTIVE ONLINE VISUALIZATION OF COMPLEX 3D GEOMETRIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 173–80. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-173-2019.

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<p><strong>Abstract.</strong> In the last decade 3D datasets of the Cultural Heritage field have become extremely rich and high detailed due to the evolution of the technologies they derive from. However, their online deployment, both for scientific and general public purposes is usually deficient in user interaction and multimedia integration. A single solution that efficiently addresses these issues is presented in this paper. The developed framework provides an interactive and lightweight visualization of high-resolution 3D models in a web browser. It is based on 3D Heritage Online Presenter (3DHOP) and Three.js library, implemented on top of WebGL API. 3DHOP capabilities are fully exploited and enhanced with new, high level functionalities. The approach is especially suited to complex geometry and it is adapted to archaeological and architectural environments. Thus, the multi-dimensional documentation of the archaeological site of Meteora, in central Greece is chosen as the case study. Various navigation paradigms are implemented and the data structure is enriched with the incorporation of multiple 3D model viewers. Furthermore, a metadata repository, comprises ortho-images, photographic documentation, video and text, is accessed straight forward through the inspection of the main 3D scene of Meteora by a system of interconnections.</p>

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Zhu, Yuan, Sami Demiroluk, Kaan Ozbay, Kun Xie, Hong Yang, and Di Sha. "SAVE-T: Safety Analysis Visualization and Evaluation Tool." Journal of Advanced Transportation 2021 (July 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/5545117.

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Traffic crashes are one of the biggest issues which constitute a threat to lives of the motorists and disrupt operations of the transportation system. To reduce the number of crashes and alleviate their impacts, it is necessary to scrutinize the factors contributing to the risk of traffic crashes. Lately, visual analytics tools become very popular for data exploration and obtaining insights from the data. In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. This tool enables users to interactively create queries and visually explore the results. By utilizing an online crash database, it offers various innovative functionalities for analysis and visualization of the crash data such as custom query development module and a subway-like map for easily visualizing the accident on the roadway segments. This tool provides an effective and efficient way to transportation agencies and professionals for traffic safety analyses and visualizations.

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Razmi, Nurul Atiqah, Muhammad Zharif Zamri, Sharifah Syafiera Syed Ghazalli, and Noraini Seman. "Visualizing stemming techniques on online news articles text analytics." Bulletin of Electrical Engineering and Informatics 10, no. 1 (February 1, 2021): 365–73. http://dx.doi.org/10.11591/eei.v10i1.2504.

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Stemming is the process to convert words into their root words by the stemming algorithm. It is one of the main processes in text analytics where the text data needs to go through stemming process before proceeding to further analysis. Text analytics is a very common practice nowadays that is practiced toanalyze contents of text data from various sources such as the mass media and media social. In this study, two different stemming techniques; Porter and Lancaster are evaluated. The differences in the outputs that are resulted from the different stemming techniques are discussed based on the stemming error and the resulted visualization. The finding from this study shows that Porter stemming performs better than Lancaster stemming, by 43%, based on the stemming error produced. Visualization can still be accommodated by the stemmed text data but some understanding of the background on the text data is needed by the tool users to ensure that correct interpretation can be made on the visualization outputs.

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Shamim, Azra, Muhammad Ahsan Qureshi, Farhana Jabeen, Misbah Liaqat, Muhammad Bilal, Yalew Zelalem Jembre, and Muhammad Attique. "Multi-Attribute Online Decision-Making Driven by Opinion Mining." Mathematics 9, no. 8 (April 11, 2021): 833. http://dx.doi.org/10.3390/math9080833.

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With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

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Dam, Søren H., Rasmus U. W. Friis, Søren D. Petersen, Andrea Martos-Esteban, and Andreas H. Laustsen. "Snake Venomics Display: An online toolbox for visualization of snake venomics data." Toxicon 152 (September 2018): 60–64. http://dx.doi.org/10.1016/j.toxicon.2018.07.019.

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Olanda, Ricardo, Mariano Pérez, Juan Manuel Orduña, and Silvia Rueda. "Terrain data compression using wavelet-tiled pyramids for online 3D terrain visualization." International Journal of Geographical Information Science 28, no. 2 (November 2013): 407–25. http://dx.doi.org/10.1080/13658816.2013.829920.

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Peng, Gang, Rashaun Wilson, Yishuo Tang, TuKiet T. Lam, Angus C. Nairn, Kenneth Williams, and Hongyu Zhao. "ProteomicsBrowser: MS/proteomics data visualization and investigation." Bioinformatics 35, no. 13 (November 21, 2018): 2313–14. http://dx.doi.org/10.1093/bioinformatics/bty958.

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Abstract Summary Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. Availability and implementation ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. Supplementary information Supplementary data are available at Bioinformatics online.

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Ehlers, Raymond, and James Mulligan. "Alice Overwatch: Online Monitoring and Data Quality Assurance Using Hlt Data." EPJ Web of Conferences 214 (2019): 01038. http://dx.doi.org/10.1051/epjconf/201921401038.

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ALICE Overwatch is a project started in late 2015 to provide augmented online monitoring and data quality assurance utilizing time-stamped QA histograms produced by the ALICE High Level Trigger. The system receives the data via ZeroMQ, stores it for later review, enriches it with detector specific functionality, and visualizes it via a web application. These provided capabilities are complementary to the existing Data Quality Monitoring system. In addition to basic visualization, additional processing options are accessible to the user, including requests for data within a particular time range or reprocessing of a particular run with different processing parameters. For example, the first ten minutes of a particular run could be investigated for a transient hot trigger channel. Due to similarities between the Overwatch architecture and that which will be used for Quality Control (QC) in LHC Run 3 and beyond, Overwatch will also be utilized to develop and test various QC components during LHC Run 2. Some of the areas of QC development include a new trending and alarm framework. We report on the project᠙s design, development, and status.

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Chen, Jianhui, and Jing Zhao. "An Educational Data Mining Model for Supervision of Network Learning Process." International Journal of Emerging Technologies in Learning (iJET) 13, no. 11 (November 9, 2018): 67. http://dx.doi.org/10.3991/ijet.v13i11.9599.

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To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.

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Soloviev, Igor, Giuseppe Avolio, Andrei Kazymov, and Matei Vasile. "ATLAS Operational Monitoring Data Archival and Visualization." EPJ Web of Conferences 245 (2020): 01020. http://dx.doi.org/10.1051/epjconf/202024501020.

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The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3.

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Ullah, Ehsan, Michaël Aupetit, Arun Das, Abhishek Patil, Noora Al Muftah, Reda Rawi, Mohamad Saad, and Halima Bensmail. "KinVis: a visualization tool to detect cryptic relatedness in genetic datasets." Bioinformatics 35, no. 15 (December 24, 2018): 2683–85. http://dx.doi.org/10.1093/bioinformatics/bty1028.

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Abstract Motivation It is important to characterize individual relatedness in terms of familial relationships and underlying population structure in genome-wide association studies for correct downstream analysis. The characterization of individual relatedness becomes vital if the cohort is to be used as reference panel in other studies for association tests and for identifying ethnic diversities. In this paper, we propose a kinship visualization tool to detect cryptic relatedness between subjects. We utilize multi-dimensional scaling, bar charts, heat maps and node-link visualizations to enable analysis of relatedness information. Availability and implementation Available online as well as can be downloaded at http://shiny-vis.qcri.org/public/kinvis/. Supplementary information Supplementary data are available at Bioinformatics online.

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Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (November 25, 2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

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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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Greussing, Esther, Sabrina Heike Kessler, and Hajo G. Boomgaarden. "Learning From Science News via Interactive and Animated Data Visualizations: An Investigation Combining Eye Tracking, Online Survey, and Cued Retrospective Reporting." Science Communication 42, no. 6 (October 13, 2020): 803–28. http://dx.doi.org/10.1177/1075547020962100.

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Relying on a multimethod approach with eye tracking, cued retrospective reporting, and a memory test, this experimental study ( N = 45) shows how individuals engage with static, interactive, and interactive-animated data visualizations embedded in online science news. The results suggest that interactivity and animation engage participants most strongly: The second part of the news article is fixated the longest by participants exposed to the interactive-animated visualization, which translates into higher learning outcomes. However, the dynamic process of news reception requires a nuanced understanding of how users attend to visual and textual parts of a message to make informed statements about their effectiveness.

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Lasheng, Yu, Wu Xu, and Yang Yu. "Research on visualization methods of online education data based on IDL and hadoop." International Journal of Advanced Computer Research 7, no. 31 (June 6, 2017): 136–46. http://dx.doi.org/10.19101/ijacr.2017.731007.

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Kötter, Rolf. "Online Retrieval, Processing, and Visualization of Primate Connectivity Data From the CoCoMac Database." Neuroinformatics 2, no. 2 (2004): 127–44. http://dx.doi.org/10.1385/ni:2:2:127.

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Yu, Shidong, Dongsheng Yang, Ying Hao, Mengjia Lian, and Ying Zang. "Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 11 (February 25, 2020): 2059036. http://dx.doi.org/10.1142/s0218001420590363.

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Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

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Pragarauskaitė, Julija, and Gintautas Dzemyda. "Visual decisions in the analysis of customers online shopping behavior." Nonlinear Analysis: Modelling and Control 17, no. 3 (August 22, 2019): 355–68. http://dx.doi.org/10.15388/na.17.3.14061.

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The analysis of the online customer shopping behavior is an important task nowadays, which allows maximizing the efficiency of advertising campaigns and increasing the return of investment for advertisers. The analysis results of online customer shopping behavior are usually reviewed and understood by a non-technical person; therefore the results must be displayed in the easiest possible way. The online shopping data is multidimensional and consists of both numerical and categorical data. In this paper, an approach has been proposed for the visual analysis of the online shopping data and their relevance. It integrates several multidimensional data visualization methods of different nature. The results of the visual analysis of numerical data are combined with the categorical data values. Based on the visualization results, the decisions on the advertising campaign could be taken in order to increase the return of investment and attract more customers to buy in the online e-shop.

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Holdorf, Amy D., Daniel P. Higgins, Anne C. Hart, Peter R. Boag, Gregory J. Pazour, Albertha J. M. Walhout, and Amy K. Walker. "WormCat: An Online Tool for Annotation and Visualization of Caenorhabditis elegans Genome-Scale Data." Genetics 214, no. 2 (December 6, 2019): 279–94. http://dx.doi.org/10.1534/genetics.119.302919.

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The emergence of large gene expression datasets has revealed the need for improved tools to identify enriched gene categories and visualize enrichment patterns. While gene ontogeny (GO) provides a valuable tool for gene set enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, genes from some model systems are not well represented. For example, ∼30% of Caenorhabditis elegans genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans gene sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify coexpressed gene sets and scaled heat map for enrichment visualization. We tested the performance of WormCat using a variety of published transcriptomic datasets, and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs, where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations, but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a gene set with potential. phenotypic relevance not found with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of gene set enrichment in different types of C. elegans datasets.

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Spiller, Moritz, Ying-Hsang Liu, Md Zakir Hossain, Tom Gedeon, Julia Geissler, and Andreas Nürnberger. "Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems." ACM Transactions on Interactive Intelligent Systems 11, no. 2 (May 20, 2021): 1–25. http://dx.doi.org/10.1145/3446638.

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Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension, however, depends on individual factors such as their cognitive abilities. The research literature provides evidence that user-adaptive information visualizations positively impact the users’ performance in visualization tasks. This study attempts to contribute toward the development of a computational model to predict the users’ success in visual search tasks from eye gaze data and thereby drive such user-adaptive systems. State-of-the-art deep learning models for time series classification have been trained on sequential eye gaze data obtained from 40 study participants’ interaction with a circular and an organizational graph. The results suggest that such models yield higher accuracy than a baseline classifier and previously used models for this purpose. In particular, a Multivariate Long Short Term Memory Fully Convolutional Network shows encouraging performance for its use in online user-adaptive systems. Given this finding, such a computational model can infer the users’ need for support during interaction with a graph and trigger appropriate interventions in user-adaptive information visualization systems. This facilitates the design of such systems since further interaction data like mouse clicks is not required.

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Jo, Sunhwa, Beomjun Park, Suan Lee, and Jinho Kim. "OLGAVis: On-Line Graph Analysis and Visualization for Bibliographic Information Network." Applied Sciences 11, no. 9 (April 24, 2021): 3862. http://dx.doi.org/10.3390/app11093862.

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Real-world systems that are composed of various types of components, their interactions, and relationships, and numerous applications are often modeled as graphs or network structures to represent and analyze the presence of relationship, shape, and meaning of objects. Network-structured data are used for various exploration and in-depth analysis through visualization of information in various fields. In particular, online bibliographic databases are a service that is used for a myriad of purposes, such as simple search of research materials as well as understanding the history and flow of research, current status, and trends. A visualization tool that can intuitively perform exploration and analysis by modeling the data provided by the online bibliographic database in a network structure will be a very meaningful study for the exploration of various information using a large amount of complex bibliographic data. This study has modeled an online bibliographic database as an information network, and further developed a prototype of a visualization tool that provides an interactive interface for easily and efficiently performing visual exploration and multidimensional analysis. The visualization tool that was developed through this study will be used to conveniently perform various online analysis of the bibliographic data, and the information and knowledge acquired as a result of the analysis are expected to contribute to the research development of various researchers. Furthermore, this visualization tool can be applied to other types of data in the future, and it is expected to develop into a useful tool for various information network analysis by improving, supplementing, and expanding the functions and performance of the developed prototype.

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Bernasconi, Anna, and Silvia Grandi. "A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic." Information 12, no. 2 (February 6, 2021): 69. http://dx.doi.org/10.3390/info12020069.

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Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

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Kuosa, Kirsi, Damiano Distante, Anne Tervakari, Luigi Cerulo, Alejandro Fernández, Juho Koro, and Meri Kailanto. "Interactive Visualization Tools to Improve Learning and Teaching in Online Learning Environments." International Journal of Distance Education Technologies 14, no. 1 (January 2016): 1–21. http://dx.doi.org/10.4018/ijdet.2016010101.

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This paper presents two interactive visualization tools for learning management systems (LMS) in order to improve learning and teaching in online courses. The first tool was developed at the Intelligent Information Systems Laboratory (IISLab) at the Tampere University of Technology (TUT). The tool is used to analyse students' activity from automatically recorded user log data and to build interactive visualizations. They provide valuable insights into the learning process and participation of students in a course offered to teachers and students. The second tool was developed at the Unitelma Sapienza University. It extends navigation and search functionalities in the discussion forum of an LMS with a topic-driven paradigm. The tool analyses forum content and automatically identifies discussion topics. It then enhances the original forum with a topic-driven navigation structure and an interactive search graph. Both tools have been developed as plug-ins for the Moodle LMS, but their analysis processes and techniques can be adopted into any LMS.

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Li, Kai, Marc Vaudel, Bing Zhang, Yan Ren, and Bo Wen. "PDV: an integrative proteomics data viewer." Bioinformatics 35, no. 7 (August 30, 2018): 1249–51. http://dx.doi.org/10.1093/bioinformatics/bty770.

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Abstract Summary Data visualization plays critical roles in proteomics studies, ranging from quality control of MS/MS data to validation of peptide identification results. Herein, we present PDV, an integrative proteomics data viewer that can be used to visualize a wide range of proteomics data, including database search results, de novo sequencing results, proteogenomics files, MS/MS data in mzML/mzXML format and data from public proteomics repositories. PDV is a lightweight visualization tool that enables intuitive and fast exploration of diverse, large-scale proteomics datasets on standard desktop computers in both graphical user interface and command line modes. Availability and implementation PDV software and the user manual are freely available at http://pdv.zhang-lab.org. The source code is available at https://github.com/wenbostar/PDV and is released under the GPL-3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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Balla, Dániel, Marianna Zichar, Róbert Tóth, Emőke Kiss, Gergő Karancsi, and Tamás Mester. "Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools." Applied Sciences 10, no. 19 (September 25, 2020): 6701. http://dx.doi.org/10.3390/app10196701.

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The appearance of online map services and frameworks (e.g., KML, QGIS) has increased the possibilities to easily, quickly and—in many cases—cost-effectively publish spatial data stored in databases. The aim of this study is to present the geovisualization of spatial databases of a Hungarian settlement on the web, using the open source webGIS system and Google application programming interfaces (APIs). The interactive point and interpolated distribution maps available online provide a detailed picture of the level of contamination, the spatial distribution of the ground water supply of the investigated settlement, and the changes which have occurred following the establishment of the sewage system. In the case of PO43− we determined that in the year before the sewage system was constructed, most of the area of the settlement could be considered contaminated, with the highest level of contamination measured in the central area of the settlement. Five years after the construction of the sewage system, the ratio of the contaminated areas had significantly decreased. In the case of NO3− we found a high level of contamination before the construction of the sewage system with a concentration increase in a North–West direction. After the establishment of the sewage system, the increase in concentration was the most intense in the central parts of the settlement, while the characteristic spatial distribution could no longer be observed. The geovisualization techniques developed are able to provide information about the different spatial data for users in a visual way, and also help to understand better the spatial information using a cognitive approach. The advantage of interactive web maps created with the technologies applied over traditional static maps is a new approach, which allows the user to manipulate the temporal and spatial data directly in the most appropriate way.

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Netek, Rostislav, and Jakub Konicek. "Interactive Spatial Visualization of Aggregated Non-spatial Coronavirus Data." Abstracts of the ICA 2 (October 8, 2020): 1–2. http://dx.doi.org/10.5194/ica-abs-2-12-2020.

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Abstract. The article describes the process of aggregation of media-based data about coronavirus pandemic in the Olomouc region, Czech Republic. Originally non-spatially located news from different sources and various platforms (government, social media, news portals) were automatically aggregated into a centralized database. The application “COVID-map” is an interactive web map solution which visualizes records from the database in a spatial way. COVID-map has been developed within Ad hoc online hackathon as an academic project at the Department of Geoinformatics, Palacký University Olomouc, Czech Republic. Alongside spatially localized data, map application collects statistical data from official sources e.g. from the governmental crisis management office.Our approach focuses on cartographical aspects of COVID-map solution. It respects current trends in both development of cartographic oriented outputs and web-based interactive map application. It is fully responsive. Originally, nonspatial data were aggregated, verified, geo-located, and finally visualized in the map environment. The layout combines three main parts: interactive map with spatial data; information panel with updated statistical data; and a sidebar with a filterable list of verified and geo-located news. The main cartographic method used for the visualisation is the choropleth map. It shows the distribution of positively tested people per 10 000 inhabitants for each sub-region. In comparison with similar coronavirus visualisation made during the current pandemic, our solution respects fundamental cartographical rules.Following cartographical topics are discussed in the article: the correct choice of cartographic method and its implementation; determination of a colour scale; recalculation statistics data into relative units by choropleths map method; design of map symbols and legend; searching for a suitable visualization of spatial and non-spatial sources into map layout; discussion on the suitability of using a clustering method.The motivation for this project was both social responsibility and dissatisfaction with the wrong implementation of basic cartographic methods in a variety of alternative solutions. The impact was immediate. Within a few days after the launch, tens of thousands users per day visited the COVID-map. It has been published by regional and national media. The COVID-map solution could be considered as a suitable ad-hoc implementation of the correctly used cartographical method on the example of coronavirus pandemic. The application “COVID-map” is available online at URL: https://gis.upol.cz/covid/mapa/.

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Katoh, Kazutaka, John Rozewicki, and Kazunori D. Yamada. "MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization." Briefings in Bioinformatics 20, no. 4 (September 6, 2017): 1160–66. http://dx.doi.org/10.1093/bib/bbx108.

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Abstract This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.

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Journal articles on the topic 'Online data visualization'

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