Visual Analytics

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 Visual Analytics;  visual data mining;  high-dimensional data;  visualization in earth space and environmental sciences;  

Abstract: The 1962 Cuban Missile Crisis was a pivotal time in the history of civilization. Many difficult decisions were made by a plethora of persons having different backgrounds, motives, beliefs, and intentions—while under extraordinarily intense pressure. In our paper, we discuss an approach designed for exploratory analysis of the decisions made using ontologies and based in visual analytics. With customized software to support our work, we will demonstrate how the Cuban Missile Crisis unfolded with a novel approach.

 ontology;  Cuban Missile Crisis;  Visual Analytics;  

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 Visual Analytics;  mobile visualization;  emergency response;  

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 Visual Analytics;  visual literature analysis;  literature fingerprinting;  

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 Visual Analytics;  multivariate data;  parallel coordinates;  dynamic queries;  iterative analysis;  starplot;  small multiples;  

Abstract: A story is a powerful abstraction used by intelligence analysts to conceptualize threats and understand patterns as part of the analytical process. This paper demonstrates a system that detects geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. The GeoTime geotemporal event visualization tool was augmented with a story system that uses narratives, hypertext linked visualizations, visual annotations, and pattern detection to create an environment for analytic exploration and communication, thereby assisting the analyst in identifying, extracting, arranging and presenting stories within the data The story system lets analysts operate at the story level with higher-level abstractions of data, such as behaviors and events, while staying connected to the evidence. The story system was developed and evaluated in collaboration with analysts.

 Visual Analytics;  narrative;  human information interaction;  sense-making;  pattern detection;  story making;  storytelling;  

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Abstract: A story is a powerful abstraction used by intelligence analysts to conceptualize threats and understand patterns as part of the analytical process. This paper demonstrates a system that detects geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. The GeoTime geotemporal event visualization tool was augmented with a story system that uses narratives, hypertext linked visualizations, visual annotations, and pattern detection to create an environment for analytic exploration and communication, thereby assisting the analyst in identifying, extracting, arranging and presenting stories within the data The story system lets analysts operate at the story level with higher-level abstractions of data, such as behaviors and events, while staying connected to the evidence. The story system was developed and evaluated in collaboration with analysts.

 Visual Analytics;  narrative;  human information interaction;  sense-making;  pattern detection;  story making;  storytelling;  

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Abstract: Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users’ exploration in such environments is usually impeded due to several problems: 1) valuable information is hard to discover when too much data is visualized on the screen; 2) Users have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists; 3) their discoveries based on visual exploration alone may lack accuracy; and 4)they have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical toolsmust be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users’ visual exploration processes. Specifically, NMS first helps users extract the valuable information (nuggets) hidden in datasets based on their interests. Given that similar nuggets may be rediscovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, localized data mining techniques are applied to refine the nuggets to best represent the captured patterns in datasets. Visualization techniques are then employed to present our collected nugget pool and thus create the nugget view. Based on the nugget view, interaction techniques are designed to help users observe and organize the nuggets in a more intuitive manner and eventually faciliate their sense-making process. We integrated NMS into XmdvTool, a freeware multivariate visualization system. User studies were performed to compare the users’ efficiency and accuracy in finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS.

 Visual Analytics;  Knowledge Discovery;;  

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Abstract: Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

 Information Visualization;  taxonomy;  Visual Analytics;  interaction techniques;  interaction;  

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Abstract: Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

 Information Visualization;  taxonomy;  Visual Analytics;  interaction techniques;  interaction;  

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 Visual Analytics;  visual knowledge discovery;  discovery management;  analysis guided exploration;  

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 Information Visualization;  Visual Analytics;  data management;  data retrieval;  smart aggregation;  situational awareness;  

Abstract: This paper introduces a new Visual Analysis tool named IMAS (Interactive Multigenomic Analysis System), which combines common analysis tools such as Glimmer, BLAST, and Clustal-W into a unified Visual Analytic framework. IMAS displays the primary DNA sequence being analyzed by the biologist in a highly interactive, zoomable visual display. The user may analyze the sequence in a number of ways, and visualize these analyses in a coherent, sequence aligned form, with all related analysis products grouped together. This enables the user to rapidly perform analyses of DNA sequences without the need for tedious and error-prone cutting and pasting of sequence data from text files to and from web-based databases and data analysis services, as is now common practice.

 Information Visualization;  Visual Analytics;  bioinformatics;  intelligence analysis;  investigative analysis;  multiple views;  

Abstract: Sorry no abstract available for this article

 knowledge discovery;  Visual Analytics;  geo-temporal visualization;  text processing;  geospatial analytics;  

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 Information Visualization;  Geovisualization;  Visual Analytics;  multivariate and spatio-temporal data;  ordering;  self-organizing map (SOM);  small multiples.;  

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 Visual Analytics;  Visualization;  visual knowledge discovery;  collaborative and distributed;  data management and knowledge representation;  

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 user interfaces;  Visual Analytics;  visual data mining;  data cleaning and integration;  

Abstract: Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysts process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets

 Visual Analytics;  Visualization;  infovis;  

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Abstract: Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.

 Visual Analytics;  

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Abstract: In today’s applications data is produced at unprecedented rates. While the capacity to collect and store new data grows rapidly, the ability to analyze these data volumes increases at much lower pace. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information "concealed" in the data. The emerging field of visual analytics focuses on handling massive, heterogenous, and dynamic volumes of information through integration of human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, and details the most important technical research challenges in the field.

 Visual Analytics;  

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 Visual Analytics;  

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 Visual Analytics;  

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 Visual Analytics;  

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 Visual Analytics;  

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 Visual Analytics;  

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 Visual Analytics;  geospatial analytics;  spatiotemporal ontology;  thematic ontology;  geospatial semantics;  semantic proximity;  spatiotemporal thematic (STTP) proximity;  

Abstract: Advances in graphical technology have now made it possible for us to interact with information in innovative ways, most notably by exploring multimedia environments and by manipulating three-dimensional virtual worlds. Many benefits have been claimed for this new kind of interactivity, a general assumption being that learning and cognitive processing are facilitated. We point out, however, that little is known about the cognitive value of any graphical representations, be they good old-fashioned (e.g. diagrams) or more advanced (e.g. animations, multimedia, virtual reality). In our paper, we critique the disparate literature on graphical representations, focusing on four representative studies. Our analysis reveals a fragmented and poorly understood account of how graphical representations work, exposing a number of assumptions and fallacies. As an alternative we propose a new agenda for graphical representation research. This builds on the nascent theoretical approach within cognitive science that analyses the role played by external representations in relation to internal mental ones. We outline some of the central properties of this relationship that are necessary for the processing of graphical representations. Finally, we consider how this analysis can inform the selection and design of both traditional and advanced forms of graphical technology.

 Visual Analytics;  cognition;  Cognitive Traced;  

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