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Готові списки джерел за темами / Interactive and visual analytics / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Interactive and visual analytics.

Статті в журналах з теми "Interactive and visual analytics"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

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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2

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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Анотація:

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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3

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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Анотація:

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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4

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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5

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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Анотація:

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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6

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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Анотація:

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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7

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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8

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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9

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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Анотація:

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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10

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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Анотація:

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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11

Casey, Fergal, Soumya Negi, Jing Zhu, et al. "OmicsView: Omics data analysis through interactive visual analytics." Computational and Structural Biotechnology Journal 20 (2022): 1277–85. http://dx.doi.org/10.1016/j.csbj.2022.02.022.

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12

Leung, Carson K. S., Christopher L. Carmichael, Patrick Johnstone, and David Sonny Hung-Cheung Yuen. "Interactive Visual Analytics of Databases and Frequent Sets." International Journal of Information Retrieval Research 3, no. 4 (2013): 120–40. http://dx.doi.org/10.4018/ijirr.2013100107.

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Анотація:

In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understandi

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13

Wong, P. C., H. Foote, P. Mackey, K. Perrine, and G. Chin. "Generating Graphs for Visual Analytics through Interactive Sketching." IEEE Transactions on Visualization and Computer Graphics 12, no. 6 (2006): 1386–98. http://dx.doi.org/10.1109/tvcg.2006.91.

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14

Chelaru, Florin, Llewellyn Smith, Naomi Goldstein, and Héctor Corrada Bravo. "Epiviz: interactive visual analytics for functional genomics data." Nature Methods 11, no. 9 (2014): 938–40. http://dx.doi.org/10.1038/nmeth.3038.

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15

Wang, Xiaoyu, Wenwen Dou, Shen-En Chen, William Ribarsky, and Remco Chang. "An Interactive Visual Analytics System for Bridge Management." Computer Graphics Forum 29, no. 3 (2010): 1033–42. http://dx.doi.org/10.1111/j.1467-8659.2009.01708.x.

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16

Sherkat, Ehsan, Evangelos E. Milios, and Rosane Minghim. "A Visual Analytics Approach for Interactive Document Clustering." ACM Transactions on Interactive Intelligent Systems 10, no. 1 (2020): 1–33. http://dx.doi.org/10.1145/3241380.

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17

Pan, Yaohua, Zhibin Niu, Jing Wu, and Jiawan Zhang. "InSocialNet: Interactive visual analytics for role—event videos." Computational Visual Media 5, no. 4 (2019): 375–90. http://dx.doi.org/10.1007/s41095-019-0157-9.

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18

Dowling, Michelle, Nathan Wycoff, Brian Mayer, et al. "Interactive Visual Analytics for Sensemaking with Big Text." Big Data Research 16 (July 2019): 49–58. http://dx.doi.org/10.1016/j.bdr.2019.04.003.

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19

Andrienko, Natalia, and Gennady Andrienko. "Visual analytics of movement: An overview of methods, tools and procedures." Information Visualization 12, no. 1 (2012): 3–24. http://dx.doi.org/10.1177/1473871612457601.

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Анотація:

Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics, particular

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20

Siddiqui, Tarique, Paul Luh, Zesheng Wang, Karrie Karahalios, and Aditya G. Parameswaran. "Expressive querying for accelerating visual analytics." Communications of the ACM 65, no. 7 (2022): 85–94. http://dx.doi.org/10.1145/3535337.

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Анотація:

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and time-consuming. In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching. To address them, we first present our work on Zenvisage that helps enumerate large collections of visual

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21

Vuckovic, Milena, and Johanna Schmidt. "On Sense Making and the Generation of Knowledge in Visual Analytics." Analytics 1, no. 2 (2022): 98–116. http://dx.doi.org/10.3390/analytics1020008.

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Анотація:

Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perception and cognition, in which human analysts perceive external representations (system structure, dataset, integral data visualizations) and form respective internal representations (internal cognitive imprints of external systems) that enable deeper comprehension of the employed system and the underlying data features. These internal representations f

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22

Zheng, Nan, Meng Sun, and Ye Yang. "Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5398932.

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Анотація:

In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students’ sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper pres

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23

Soriano-Vargas, Aurea, Bernd Hamann, and Maria Cristina F de Oliveira. "TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data." Information Visualization 19, no. 1 (2019): 3–23. http://dx.doi.org/10.1177/1473871619858937.

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We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualizati

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24

Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics 22, no. 5 (2015): 545–70. http://dx.doi.org/10.5194/npg-22-545-2015.

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Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-ref

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25

Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics Discussions 2, no. 2 (2015): 709–80. http://dx.doi.org/10.5194/npgd-2-709-2015.

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Анотація:

Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-ref

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26

Ding, Linfang, Liqiu Meng, Jian Yang, and Jukka M. Krisp. "Interactive visual exploration and analysis of origin-destination data." Proceedings of the ICA 1 (May 16, 2018): 1–5. http://dx.doi.org/10.5194/ica-proc-1-29-2018.

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Анотація:

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel

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27

Lettieri, Nicola, Antonio Altamura, and Delfina Malandrino. "The legal macroscope: Experimenting with visual legal analytics." Information Visualization 16, no. 4 (2016): 332–45. http://dx.doi.org/10.1177/1473871616681374.

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Анотація:

This work presents Knowlex, a web application designed for visualization, exploration, and analysis of legal documents coming from different sources. Understanding the legal framework relating to a given issue often requires the analysis of complex legal corpora. When a legal professional or a citizen tries to understand how a given phenomenon is disciplined, his attention cannot be limited to a single source of law but has to be directed on the bigger picture resulting from all the legal sources related to the theme under investigation. Knowlex exploits data visualization to support this acti

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28

Jeong, Dong Hyun, Caroline Ziemkiewicz, Brian Fisher, William Ribarsky, and Remco Chang. "iPCA: An Interactive System for PCA-based Visual Analytics." Computer Graphics Forum 28, no. 3 (2009): 767–74. http://dx.doi.org/10.1111/j.1467-8659.2009.01475.x.

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29

Schatz, Michael C., Adam M. Phillippy, Ben Shneiderman, and Steven L. Salzberg. "Hawkeye: an interactive visual analytics tool for genome assemblies." Genome Biology 8, no. 3 (2007): R34. http://dx.doi.org/10.1186/gb-2007-8-3-r34.

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30

Rostamzadeh, Neda, Sheikh S. Abdullah, and Kamran Sedig. "Visual Analytics for Electronic Health Records: A Review." Informatics 8, no. 1 (2021): 12. http://dx.doi.org/10.3390/informatics8010012.

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Анотація:

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years,

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31

Chinchor, Nancy, and William A. Pike. "The Science of Analytic Reporting." Information Visualization 8, no. 4 (2009): 286–93. http://dx.doi.org/10.1057/ivs.2009.21.

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The challenge of visually communicating analysis results is central to the ability of visual analytics tools to support decision making and knowledge construction. The benefit of emerging visual methods will be improved through more effective exchange of the insights generated through the use of visual analytics. This article outlines the major requirements for next-generation reporting systems in terms of eight major research needs: the development of best practices, design automation, visual rhetoric, context and audience, connecting analysis to presentation, evidence and argument, collabora

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32

Ebrahim, H., T. Santhanavanich, P. Wuerstle, and V. Coors. "CONCEPT AND EVALUATION OF AN URBAN PLATFORM FOR INTERACTIVE VISUAL ANALYTICS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W1-2021 (September 3, 2021): 33–40. http://dx.doi.org/10.5194/isprs-annals-viii-4-w1-2021-33-2021.

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Abstract. Urban platforms are becoming a vital role player in city resources management for achieving the right balance between social and economic services and their impact on the environment. More and more cities are starting to benefit from an urban platform to state the city conditions and re-coin the shape of life depending on data gathered from different city systems. However, urban platforms need further support of data analytics in respect of reaching a smart city platform helping city planners with better decision making. Besides, the majority of operational urban platforms consider 2

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33

Hirve, Sumit Arun, and Pradeep Reddy C. H. "Improving Big Data Analytics With Interactive Augmented Reality." International Journal of Information System Modeling and Design 13, no. 7 (2022): 1–11. http://dx.doi.org/10.4018/ijismd.315124.

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Анотація:

Since, data is generated every minute by everyone including consumers and/or business worldwide, there is an enormous worth for big data analytics. Big data analytics is a technique for extracting important information from large amounts of a data. Visualization is the best medium to analyze and share information. Visual images help to transmit bid data to the human brain within a few seconds. Visual interpretations help in visualizing data from different angles. Visualization helps to outline problems and understand current trends. Augmented reality enables the user to experience the real wor

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34

Rostamzadeh, Neda, Sheikh S. Abdullah, Kamran Sedig, Amit X. Garg, and Eric McArthur. "Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results." Informatics 9, no. 1 (2022): 17. http://dx.doi.org/10.3390/informatics9010017.

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Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the predictio

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35

Popelka, Stanislav, Lukáš Herman, Tomas Řezník, et al. "User Evaluation of Map-Based Visual Analytic Tools." ISPRS International Journal of Geo-Information 8, no. 8 (2019): 363. http://dx.doi.org/10.3390/ijgi8080363.

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Big data have also become a big challenge for cartographers, as the majority of big data may be localized. The use of visual analytics tools, as well as comprising interactive maps, stimulates inter-disciplinary actors to explore new ideas and decision-making methods. This paper deals with the evaluation of three map-based visual analytics tools by means of the eye-tracking method. The conceptual part of the paper begins with an analysis of the state-of-the-art and ends with the design of proof-of-concept experiments. The verification part consists of the design, composition, and realization o

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36

Grigorieva, Maria, Mikhail Titov, Timofei Galkin, and Igal Milman. "Nested Intellectual Data Grouping and Clusterization for the Interactive Visual Explorer." EPJ Web of Conferences 226 (2020): 03011. http://dx.doi.org/10.1051/epjconf/202022603011.

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Анотація:

The Interactive Visual Explorer (InVEx) application is designed as a visual analytics tool for Big Data analysis. Visual analytics is an integral approach to data analysis, combining methods of intellectual data analysis with advanced interactive visualization. One of the main objectives of InVExis to process large data samples by decreasing their level of detail (LoD).The proposed approach includes clustering as well as flexible grouping by different parameters, providing the exploration of data from the lowest to the highest level of details. The results of grouping and clusterization arevis

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37

Eaglin, Todd, Xiaoyu Wang, and Bill Ribarsky. "Interactive Visual Analytics in Support of Image-Encoded LiDAR Analysis." Electronic Imaging 2016, no. 1 (2016): 1–9. http://dx.doi.org/10.2352/issn.2470-1173.2016.1.vda-495.

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38

Fischer, M. T., D. Seebacher, R. Sevastjanova, D. A. Keim, and M. El‐Assady. "CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling." Computer Graphics Forum 40, no. 3 (2021): 25–36. http://dx.doi.org/10.1111/cgf.14286.

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39

Basole, Rahul C., Ahsan Qamar, Biswajyoti Pal, et al. "Understanding Failure Mode Effect Analysis Data Using Interactive Visual Analytics." IEEE Computer Graphics and Applications 39, no. 6 (2019): 17–26. http://dx.doi.org/10.1109/mcg.2019.2944230.

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40

Kandogan, E. "Just-in-time interactive analytics: Guiding visual exploration of data." IBM Journal of Research and Development 59, no. 2/3 (2015): 12:1–12:10. http://dx.doi.org/10.1147/jrd.2015.2400665.

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41

Chang, Remco, David S. Ebert, and Daniel Keim. "Introduction to the Special Issue on Interactive Computational Visual Analytics." ACM Transactions on Interactive Intelligent Systems 4, no. 1 (2014): 1–3. http://dx.doi.org/10.1145/2594648.

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42

George, Richard L. S. F., Peter E. Robins, Alan G. Davies, Panagiotis D. Ritsos, and Jonathan C. Roberts. "Interactive visual analytics of hydrodynamic flux for the coastal zone." Environmental Earth Sciences 72, no. 10 (2014): 3753–66. http://dx.doi.org/10.1007/s12665-014-3283-9.

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43

Günnemann, Nikou, Michael Derntl, Ralf Klamma, and Matthias Jarke. "An Interactive System for Visual Analytics of Dynamic Topic Models." Datenbank-Spektrum 13, no. 3 (2013): 213–23. http://dx.doi.org/10.1007/s13222-013-0134-x.

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Guo, Yuejun, Qing Xu, and Mateu Sbert. "IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering." Entropy 20, no. 3 (2018): 159. http://dx.doi.org/10.3390/e20030159.

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Jeong, Dong Hyun, Pradeep Behera, Bong Keun Jeong, Carlos David Luna Sangama, Bryan Higgs, and Soo-Yeon Ji. "Designing an Interactive Visual Analytics System for Precipitation Data Analysis." Applied Sciences 15, no. 10 (2025): 5467. https://doi.org/10.3390/app15105467.

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As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To e

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Kasik, David J., David Ebert, Guy Lebanon, Haesun Park, and William M. Pottenger. "Data Transformations and Representations for Computation and Visualization." Information Visualization 8, no. 4 (2009): 275–85. http://dx.doi.org/10.1057/ivs.2009.27.

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At the core of successful visual analytics systems are computational techniques that transform data into concise, human comprehensible visual representations. The general process often requires multiple transformation steps before a final visual representation is generated. This article characterizes the complex raw data to be analyzed and then describes two different sets of transformations and representations. The first set transforms the raw data into more concise representations that improve the performance of sophisticated computational methods. The second transforms internal representati

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Wong, Pak Chung, Stuart J. Rose, George Chin, et al. "Walking the Path: A New Journey to Explore and Discover Through Visual Analytics." Information Visualization 5, no. 4 (2006): 237–49. http://dx.doi.org/10.1057/palgrave.ivs.9500133.

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Under the leadership of the US Department of Homeland Security (DHS), researchers at the Pacific Northwest National Laboratory (PNNL) established a research center focusing on the discipline of visual analytics in 2004. A year later, the center led a multidisciplinary panel representing academia, industry, and government to formally define directions and priorities for future research and development (R&D) for visual analytics tools. The R&D agenda, Illuminating the Path, defines the term visual analytics as ‘the science of analytical reasoning facilitated by interactive visual interfa

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Rachna, Mrs Amulya. "Interactive Data Analytics for Election Monitoring: Smart Election Insights Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49912.

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Abstract—Smart Election Insights is an innovative data-driven initiative aimed at helping users whether government officials, researchers, or general citizens understand and interpret election data from India in a more accessible and meaningful way. This project aims to simplify intricate datasets related to the Indian electoral process into easy-to-understand visuals. It focuses on a variety of elements such as voter turnout, the distribution and setup of polling stations, and detailed information on contesting candidates. What sets this project apart is its use of the Python programming lang

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Santamaría, Rodrigo, Roberto Therón, Laura Durán, et al. "Genome-wide search of nucleosome patterns using visual analytics." Bioinformatics 35, no. 13 (2018): 2185–92. http://dx.doi.org/10.1093/bioinformatics/bty971.

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Abstract Motivation The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. Results We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, ‘Nucleosee‘, an interactive visual tool specifically designed to enhanc

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Varu, Rakshit, Leonardo Christino, and Fernando V. Paulovich. "ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics." Electronics 11, no. 9 (2022): 1344. http://dx.doi.org/10.3390/electronics11091344.

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Amongst the data mining techniques for exploratory analysis, association rule mining is a popular strategy given its ability to find causal rules between items to express regularities in a database. With large datasets, many rules can be generated, and visualization has shown to be instrumental in such scenarios. Despite the relative success, existing visual representations are limited and suffer from analytical capability and low interactive support issues. This paper presents ARMatrix, a visual analytics framework for the analysis of association rules based on an interactive item-to-rule mat

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