The paper is devoted to the development of tools, which enable to improve the comprehensive power of visual analytics of interconnected data. This kind of data is a great challenge for researchers in the field of Digital Humanities. We propose using ontology-driven SciVi visual analytics platform to tackle this challenge and help researchers to bring data to life. The proposed analytics components are based on the circular graph, representing the data elements as the circle distributed nodes and the data elements' connections as the cubic parabolas' arcs. SciVi platform provides not only the traditional interactive means for graph visual analytics, such as node searching based on regular expressions, highlighting of incident edges and connected nodes by mouse hover, depicting clusters by colors, threshold-based filtering of weighted nodes and edges, etc., but also a set of new features, which help to solve special analytics tasks. The paper presents these novel features and corresponding use cases. First, we propose an ontology-driven data extraction, transformation and loading mechanism that allows obtaining the input data from different sources and preprocessing them by custom algorithms defined by means of high-level visual programming language. Second, we developed a multilevel ring scale that is placed around the circular graph allowing to group the graph nodes according to the given classifier and automatically reorder them at runtime. Third, we demonstrate an implementation of the equalizing filter that allows applying different filtering thresholds to different groups of graph nodes/edges to cut off the noisy data. This is necessary for data wrangling in the case the data noise has a non-uniform strength distribution across the graph. Fourth, we developed a graph state calculator that allows data comparison by performing different operations like union, intersection, etc. on the data slices shown within the graph. Fifth, we make it possible to synchronize the data slice currently visualized by the graph with the corresponding localized area on the geographical map. Thanks to the features presented, the SciVi advanced interactive tools can harness the power of visual analytics in Digital Humanities and Big Data.
The paper is devoted to the development of software tools to support eye-trackingbased research in an immersive virtual reality environment. Eye tracking is a popular technology for studying human behavior because it provides objective metrics to estimate human perception strategies. The corresponding hardware evolves rapidly, and nowadays its ergonomics and accessibility enable to use this hardware in a wide range of research. Recently, eye tracking devices are combined with head-mounted virtual reality displays, which allows the detecting of virtual objects the user is looking at. This, in turn, opens three new development roads. First, new interaction methods emerge, when the user can select objects with a gaze. Second, new ways of presenting virtual reality become possible, like, for example, foveated rendering (graphics rendering optimization that locates by eye tracker the zone the user is looking at, increases the image quality in that zone, and decreases the image quality in the peripheral vision). Third, new opportunities emerge to carry out the eye-tracking-based research of human behavior, wherein the spectrum of possible experiments increases dramatically compared to what is achievable in the real world. In this paper, we focus on the third road.While there is a lot of mature software to support traditional eye-tracking-based experiments, virtual reality brings new challenges not yet tackled by the existing means. The main challenge is a seamless integration of eye tracking analytics tools with virtual reality engines. In the present work, we address this challenge by proposing a flexible data mining and visual analytics pipeline based on the ontology-driven platform SciVi that deeply integrates with the virtual scene rendered by Unreal Engine and displayed by HTC Vive Pro Eye head-mounted display.We are interested in using eye tracking to study the reading process in immersive virtual reality. While the reading process in normal conditions is studied quite well, there is a lack of corresponding research related to the virtual reality environment. To the best of our knowledge, currently, just one attempt is reported in the literature, considering the reading of short phrases. In contrast, we plan to examine the reading of complete texts. The aim of the present work is to develop software tools needed to support the eye-tracking-based reading experiments in virtual reality and to obtain preliminary results.To enable the visual mining of eye tracking data obtained in the reading experiments, we propose a new modification of a well-known radial transition graph that allows visually inspecting the scanpaths (sequences of eye fixations -moments when eyes are stationary -and interchanging saccades -moments when eyes rapidly move between viewing positions). Our modification is based on the SciVi::CGraph visualization module that performs well on handling large graphs and provides advanced search and filtering capabilities. The distinctiveness of our modification is the efficient tackling of the so-called "ha...
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