Relevant bibliographies by topics / Interactive and visual analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Interactive and visual analytics'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Interactive and visual analytics"

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Schumann, Heidrun, and Christian Tominski. "Analytical, visual and interactive concepts for geo-visual analytics." Journal of Visual Languages & Computing 22, no. 4 (2011): 257–67. http://dx.doi.org/10.1016/j.jvlc.2011.03.002.

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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.

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Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparkl
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Pike, William A., John Stasko, Remco Chang, and Theresa A. O'Connell. "The Science of Interaction." Information Visualization 8, no. 4 (2009): 263–74. http://dx.doi.org/10.1057/ivs.2009.22.

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There is a growing recognition within the visual analytics community that interaction and inquiry are inextricable. It is through the interactive manipulation of a visual interface–the analytic discourse–that knowledge is constructed, tested, refined and shared. This article reflects on the interaction challenges raised in the visual analytics research and development agenda and further explores the relationship between interaction and cognition. It identifies recent exemplars of visual analytics research that have made substantive progress toward the goals of a true science of interaction, wh
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Ahmed, Nesreen, and Ryan Rossi. "Interactive Visual Graph Analytics on the Web." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (2021): 566–69. http://dx.doi.org/10.1609/icwsm.v9i1.14653.

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We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particu
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Cybulski, Jacob L., Susan Keller, and Dilal Saundage. "Interactive Exploration of Data with Visual Metaphors." International Journal of Software Engineering and Knowledge Engineering 25, no. 02 (2015): 231–52. http://dx.doi.org/10.1142/s0218194015400082.

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Visual Analytics (VA) is an approach to data analysis by means of visual manipulation of data representation, which relies on innate human abilities of perception and cognition. Even though current visual toolkits in the Business Analytics (BA) domain have improved the effectiveness of data exploration, analysis and reporting, their features are often not intuitive, and can be confusing and difficult to use. Moreover, visualizations generated from these toolkits are mostly accessible to specialist users. Thus, there is a need for analytic environments that support data exploration, interpretat
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Kalinin, Alexandr A., Selvam Palanimalai, Junqi Zhu, et al. "SOCRAT: A Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization." Information 13, no. 11 (2022): 547. http://dx.doi.org/10.3390/info13110547.

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Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interface
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Shneiderman, Ben, and Catherine Plaisant. "Interactive Visual Event Analytics: Opportunities and Challenges." Computer 52, no. 1 (2019): 27–35. http://dx.doi.org/10.1109/mc.2018.2890217.

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Rossi, Ryan A., and Nesreen K. Ahmed. "An Interactive Data Repository with Visual Analytics." ACM SIGKDD Explorations Newsletter 17, no. 2 (2016): 37–41. http://dx.doi.org/10.1145/2897350.2897355.

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Kodagoda, Neesha, Simon Attfield, Tinni Choudhury, et al. "Concern level assessment: Building domain knowledge into a visual system to support network-security situation awareness." Information Visualization 13, no. 4 (2013): 346–60. http://dx.doi.org/10.1177/1473871613490291.

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Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE Visual Analytics Science and Technology challenge in which we developed a visual analytic solution to a network-security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgem
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Heer, Jeffrey, and Maneesh Agrawala. "Design Considerations for Collaborative Visual Analytics." Information Visualization 7, no. 1 (2008): 49–62. http://dx.doi.org/10.1057/palgrave.ivs.9500167.

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Visualizations leverage the human visual system to support the process of sensemaking, in which information is collected, organized, and analyzed to generate knowledge and inform action. Although most research to date assumes a single-user focus on perceptual and cognitive processes, in practice, sensemaking is often a social process involving parallelization of effort, discussion, and consensus building. Thus, to fully support sensemaking, interactive visualization should also support social interaction. However, the most appropriate collaboration mechanisms for supporting this interaction ar
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Dissertations / Theses on the topic "Interactive and visual analytics"

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Khemiri, Wael. "Data-intensive interactive workflows for visual analytics." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00659227.

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The increasing amounts of electronic data of all forms, produced by humans (e.g. Web pages, structured content such as Wikipedia or the blogosphere etc.) and/or automatic tools (loggers, sensors, Web services, scientific programs or analysis tools etc.) leads to a situation of unprecedented potential for extracting new knowledge, finding new correlations, or simply making sense of the data.Visual analytics aims at combining interactive data visualization with data analysis tasks. Given the explosion in volume and complexity of scientific data, e.g., associated to biological or physical process
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Wenskovitch, Jr John Edward. "Dimension Reduction and Clustering for Interactive Visual Analytics." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/96599.

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When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. The second, clustering, groups similar observations while simultaneously separating dissimilar observations. Existing work presents a number of systems and approaches that utilize these techniques; however, these techniques can cooperate or conflict in unexpected ways. The core contribution of
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Moran, Andrew M. Eng Massachusetts Institute of Technology. "Improving big data visual analytics with interactive virtual reality." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105972.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 80-84).<br>For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has
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Han, Chao. "Bayesian Visual Analytics: Interactive Visualization for High Dimensional Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19210.

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In light of advancements made in data collection techniques over the past two decades, data mining has become common practice to summarize large, high dimensional datasets, in hopes of discovering noteworthy data structures. However, one concern is that most data mining approaches rely upon strict criteria that may mask information in data that analysts may find useful. We propose a new approach called Bayesian Visual Analytics (BaVA) which merges Bayesian Statistics with Visual Analytics to address this concern. The BaVA framework enables experts to interact with the data and the feature disc
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Alsakran, Jamal. "Supporting Interactive Visual Analytics for High Dimensional Data Exploration." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1340683236.

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Zhao, Kaiyu. "A Model-driven Visual Analytic Framework for Local Pattern Analysis." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/446.

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The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the process of discovering useful information is becoming more challenging nowadays due to the growing data scale. Particularly, the human cognitive capabilities remain constant whereas the scale and complexity of data are not. Meanwhile, visual analytics largely relies on human analytic in the loop which imposes challenge to traditional human-driven workflow. It is almost impossible to show every aspect of details to the user while diving into local region of the data to explain phenom
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AL-Dohuki, Shamal Mohammed Ameen. "INTERACTIVE VISUAL QUERYING AND ANALYSIS FOR URBAN TRAJECTORY DATA." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1555411250877166.

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Kusoffsky, Madeleine. "Leveraging storytelling in visual analytics by redesigning the user interface." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-96724.

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Storytelling is a way of packaging the knowledge and insights gained from analyzing statistical data. The knowledge is transformed into a format that lends itself to be understood by non-experts more easily. The story with links to interactive diagrams. The purpose of this design study was to improve the interaction design of the storytelling feature. The target audience for the new design was intermediate users. Evaluation of the current design by interviewing and observing beginner and intermediate users gave valuable understanding about the users goals related to the storytelling feature. T
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Chung, HaeYong. "Designing Display Ecologies for Visual Analysis." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52042.

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The current proliferation of connected displays and mobile devices from smart phones and tablets to wall-sized displays presents a number of exciting opportunities for information visualization and visual analytics. When a user employs heterogeneous displays collaboratively to achieve a goal, they form what is known as a display ecology. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish tasks and goals. However, since information and tasks are scattered and disconnected among separate displays, one of the inherent chal
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Chen, Xin. "Be the Data: Embodied Visual Analytics." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72287.

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With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel application, Be the Data, to
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Books on the topic "Interactive and visual analytics"

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Endert, Alex. Semantic Interaction for Visual Analytics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02603-4.

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Zhang, Qingyu, Richard Segall, and Mei Cao. Visual analytics and interactive technologies: Data, text, and web mining applications. Information Science Reference, 2011.

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Amicis, Raffaele De, Radovan Stojanovic, and Giuseppe Conti, eds. GeoSpatial Visual Analytics. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0.

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Andrienko, Gennady, Natalia Andrienko, Peter Bak, Daniel Keim, and Stefan Wrobel. Visual Analytics of Movement. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37583-5.

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Khan, Arshad. Visual Analytics for Dashboards. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0119-8.

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Amicis, Raffaele De. GeoSpatial Visual Analytics: Geographical Information Processing and Visual Analytics for Environmental Security. Springer Netherlands, 2009.

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Amicis, Raffaele De. Geospatial visual analytics: Geographical information processing and visual analytics for environmental security. Springer, 2009.

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Suh, Sang C., and Thomas Anthony, eds. Big Data and Visual Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63917-8.

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Andrienko, Natalia, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, and Stefan Wrobel. Visual Analytics for Data Scientists. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56146-8.

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Scholtz, Jean. User-Centered Evaluation of Visual Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-02605-8.

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Book chapters on the topic "Interactive and visual analytics"

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Döweling, Sebastian, Florian Probst, Thomas Ziegert, and Knut Manske. "Soknos — An Interactive Visual Emergency Management Framework." In GeoSpatial Visual Analytics. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_20.

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Andrienko, Natalia, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, and Stefan Wrobel. "Principles of Interactive Visualisation." In Visual Analytics for Data Scientists. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56146-8_3.

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Nguyen, Quang Vinh, and Mao Lin Huang. "A New Interactive Platform for Visual Analytics of Social Networks." In Visual Information Communication. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0312-9_15.

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Holzinger, Andreas. "Lecture 9 Interactive Information Visualization and Visual Analytics." In Biomedical Informatics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04528-3_9.

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Keim, Daniel A., Florian Mansmann, Daniela Oelke, and Hartmut Ziegler. "Visual Analytics: Combining Automated Discovery with Interactive Visualizations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87987-9_2.

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Schöning, Julius, and Gunther Heidemann. "Visual Video Analytics for Interactive Video Content Analysis." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03402-3_23.

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Keim, Daniel A., Florian Mansmann, Daniela Oelke, and Hartmut Ziegler. "Visual Analytics: Combining Automated Discovery with Interactive Visualizations." In Discovery Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_2.

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Davey, James, Florian Mansmann, Jörn Kohlhammer, and Daniel Keim. "Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions." In The Future Internet. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30241-1_9.

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Rentschler, Andreas, Qais Noorshams, Lucia Happe, and Ralf Reussner. "Interactive Visual Analytics for Efficient Maintenance of Model Transformations." In Theory and Practice of Model Transformations. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38883-5_14.

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Endert, Alex. "Spatializations for Sensemaking Using Visual Analytics." In Semantic Interaction for Visual Analytics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02603-4_3.

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Conference papers on the topic "Interactive and visual analytics"

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Zhang, Yuqi, Adam Perer, and Will Epperson. "Guided Statistical Workflows with Interactive Explanations and Assumption Checking." In 2024 IEEE Visualization and Visual Analytics (VIS). IEEE, 2024. https://doi.org/10.1109/vis55277.2024.00013.

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Mason, Lee, Blánaid Hicks, and Jonas Almeida. "Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation." In 2024 IEEE Visualization and Visual Analytics (VIS). IEEE, 2024. https://doi.org/10.1109/vis55277.2024.00022.

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Bromley, Dennis, and Vidya Setlur. "Dash: A Bimodal Data Exploration Tool for Interactive Text and Visualizations." In 2024 IEEE Visualization and Visual Analytics (VIS). IEEE, 2024. https://doi.org/10.1109/vis55277.2024.00059.

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Bidlingmaier, Antonella, Julian Jandeleit, Fred Kunze, et al. "Fishing Vizzard: An Interactive Visual Analytics Tool to Identify Suspicious Fishing Behavior." In 2024 IEEE Visual Analytics Science and Technology VAST Challenge. IEEE, 2024. http://dx.doi.org/10.1109/vastchallenge64683.2024.00017.

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Cybulski, Jacob, Susan Keller, and Dilal Saundage. "Metaphors in Interactive Visual Analytics." In the 7th International Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2636240.2636866.

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Cancino, Waldo, Nadia Boukhelifa, and Evelyne Lutton. "EvoGraphDice: Interactive evolution for visual analytics." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256553.

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Morse, Peter, Anya Reading, Christopher Lueg, and Sarah Kenderdine. "TaggerVR: Interactive Data Analytics for Geoscience - A Novel Interface for Interactive Visual Analytics of Large Geoscientific Datasets in Cloud Repositories." In 2015 Big Data Visual Analytics (BDVA). IEEE, 2015. http://dx.doi.org/10.1109/bdva.2015.7314303.

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Gotz, David, Michelle Zhou, and Vikram Aggarwal. "Interactive Visual Synthesis of Analytic Knowledge." In 2006 IEEE Symposium On Visual Analytics And Technology. IEEE, 2006. http://dx.doi.org/10.1109/vast.2006.261430.

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Ebert, David, Brian Fisher, and Kelly Gaither. "Introduction to Interactive Visual Decision Analytics Minitrack." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2017. http://dx.doi.org/10.24251/hicss.2017.163.

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Park, Haesun. "Interactive visual analytics for high dimensional data." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013. http://dx.doi.org/10.1145/2501511.2501514.

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Reports on the topic "Interactive and visual analytics"

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Greitzer, Frank L., Christine F. Noonan, and Lyndsey Franklin. Cognitive Foundations for Visual Analytics. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1013936.

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Cook, Kristin A., Edwin R. Burtner, Brian P. Kritzstein, Brooke R. Brisbois, and Anna E. Mitson. Streaming Visual Analytics Workshop Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1417447.

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Steed, Chad A., Christopher T. Symons, James K. Senter, and Frank A. DeNap. Guided Text Search Using Adaptive Visual Analytics. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055105.

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Neiderer, Andrew M. Visual Analytics for Exploration of a High-Dimensional Structure. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada585998.

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Woodring, Jonathan Lee, Han Wei Shen, and Tom Peterka. Visual Analytics for Large Scale Scientific Simulations, Fiscal Year 2019. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1583139.

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Steed, Chad A., J. Edward SwanII, Patrick J. Fitzpatrick, and T. J. Jankun-Kelly. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1035521.

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Ma, Kwan-Liu. COLLABORATIVE: IN SITU VISUAL ANALYTICS TECHNOLOGIES FOR EXTREME SCALE COMBUSTION SIMULATIONS. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2530502.

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Emsley, Howard T. I3G VIRGO Interactive Graphics for Geometry Generation and Visual Interactive Rapid Grid Generation. User's Manual. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada226117.

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Radwan, Hassan, Dylan Halperin, Robert Ferris, and Ken Prole. Code Pulse: Software Assurance (SWA) Visual Analytics for Dynamic Analysis of Code. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610605.

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Pascuccfi, Valerio. CyclusJS: A Distributed Web-based Fuel Cycle Visual Analytics. Research Performance Final Report/. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1881220.

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