Issue on Visual Analytics

Journal of Information Technology Research (JITR) has a long tradition in publishing research papers devoted to develop new automatic and intelligent data analysis, for example this feature is pretty present in the four papers that compose current JITR issue. Artificial intelligent techniques, new algorithms, data mining approaches, agent-based solutions, etc. are usually used to do that. Also, it is very common that the performed analysis techniques are complemented with data visualization for presenting the results to the analyst in order to proceed with the decision-making processes

Geospatial analysis

This chapter is about geospatial analysis of social media. It summarizes major issues with retrieving, sampling, geocoding, and analyzing social media data. The chapter discusses geospatial analysis from the perspectives of different domains of knowledge, including information science, geographic information science, geovisualization, information visualization and visual analytics. It shows benefits and shortcomings of these approaches and defines existing gaps in geospatial analysis

Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels

© 2016 The Author(s)Typography is overlooked in knowledge maps (KM) and information retrieval (IR), and some deficiencies in these systems can potentially be improved by encoding information into font attributes. A review of font use across domains is used to itemize font attributes and information visualization theory is used to characterize each attribute. Tasks associated with KM and IR, such as skimming, opinion analysis, character analysis, topic modelling and sentiment analysis can be aided through the use of novel representations using font attributes such as skim formatting, proportional encoding, textual stem and leaf plots and multi-attribute labels

Modeling Complex High Level Interactions in the Process of Visual Mining

Visual Mining refers to the human analytical process that uses visual representations of raw data and makes suitable inferences. During this analytical process, users are engaged in complex cognitive activities such as decision making, problem solving, analytical reasoning and learning. Now a days, users typically use interactive visualization tools, which we call as visual mining support tools (VMSTs), to mediate their interactions with the information present in visual representations of raw data and also to support their complex cognitive activities when performing visual mining. VMSTs have two main components: visual representation and interaction. Even though, these two components are fundamental aspects of VMSTs, the research on visual representation has received the most attention. It is still unclear how to design interactions which can properly support users in performing complex cognitive activities during the visual mining process. Although some fundamental concepts and techniques regarding interaction design have been in place for a while, many established researchers are of the opinion that we do not yet have a generalized, principled, and systematic understanding of interaction components of these VMSTs, and how interactions should be analyzed, designed, and integrated to support complex cognitive activities. Many researchers have recommended that one way to address this problem is through appropriate characterization of interactions in the visual mining process. Models that provide classifications of interactions have indeed been proposed in the visualization research community. While these models are important contributions for the visualization research community, they often characterize interactions at lower levels of human information interaction and high level interactions are not well addressed. In addition, some of these models are not designed to model user activity; rather they are most applicable for representing a system’s response to user activity and not the user activity itself. In this thesis, we address this problem through characterization of the interaction space of visual mining at the appropriate level. Our main contribution in this research is the discovery of a small set of classification criteria which can comprehensively characterize the interaction space of visual mining involving interactions with VMSTs for performing complex cognitive activities. These complex cognitive activities are modeled through visual mining episodes, a coherent set of activities consisting of visual mining strategies (VMSs). Using the classification criteria, VMSs are simply described as combinations of different values of these criteria. By considering all combinations, we can comprehensively cover the interaction space of visual mining. Our VMS interaction space model is unique in identifying the activity tier, a granularity of interactions (high level) which supports performance of complex cognitive activities through interactions with visual information using VMSTs. As further demonstration of the utility of this VMS interaction space model, we describe the formulation of an inspection framework which can provide quantitative measures for the support provided by VMSTs for complex cognitive activities in visual mining. This inspection framework, which has enabled us to produce a new simpler evaluation method for VMSTs in comparison to existing evaluation methods, is based soundly on existing theories and models. Both the VMS interaction space model and the inspection framework present many interesting avenues for further research

The Design of Interactive Visualizations and Analytics for Public Health Data

Public health data plays a critical role in ensuring the health of the populace. Professionals use data as they engage in efforts to improve and protect the health of communities. For the public, data influences their ability to make health-related decisions. Health literacy, which is the ability of an individual to access, understand, and apply health data, is a key determinant of health. At present, people seeking to use public health data are confronted with a myriad of challenges some of which relate to the nature and structure of the data. Interactive visualizations are a category of computational tools that can support individuals as they seek to use public health data. With interactive visualizations, individuals can access underlying data, change how data is represented, manipulate various visual elements, and in certain tools control and perform analytic tasks. That being said, currently, in public health, simple visualizations, which fail to effectively support the exploration of large sets of data, are predominantly used. The goal of this dissertation is to demonstrate the benefit of sophisticated interactive visualizations and analytics. As improperly designed visualizations can negatively impact users’ discourse with data, there is a need for frameworks to help designers think systematically about design issues. Furthermore, there is a need to demonstrate how such frameworks can be utilized. This dissertation includes a process by which designers can create health visualizations. Using this process, five novel visualizations were designed to facilitate making sense of public health data. Three studies were conducted with the visualizations. The first study explores how computational models can be used to make sense of the discourse of health on a social media platform. The second study investigates the use of instructional materials to improve visualization literacy. Visualization literacy is important because even when visualizations are designed properly, there still exists a gap between how a tool works and users’ perceptions of how the tool should work. The last study examines the efficacy of visualizations to improve health literacy. Overall then, this dissertation provides designers with a deeper understanding of how to systematically design health visualizations

Iterative Visual Analytics and its Applications in Bioinformatics

Indiana University-Purdue University Indianapolis (IUPUI)You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions