Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

VAST 2020 Contest Challenge: GraphMatchMaker-Visual Analytics for Graph Comparison and Matching

We report on the process and design of our visual analytics graph analysis challenge winning entry. Specifically, our team addressed the IEEE VAST 2020 Mini-Challenge 1 that asked participants to identify a group of people that accidentally caused an internet outage. To identify this group, we were given a network profile and a large multivariate social network to search in. Our approach involved statistical and graphical analysis as well as the design of three custom visual analytics tools. The submitted solution and visualizations are available at https://graphletmatchmaker.github.io/.

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

This article discusses the relevant task of analyzing user data in the process of managing various web projects. The results of this analysis will help to improve the management of diverse web projects during crises. The authors explore the concept of data heterogeneity in web projects, classify web projects by function and purpose, and analyze the search models and data display in web projects. The proposed algorithms for analyzing user data in the process of managing diverse web projects will improve the structuring and presentation of data on the web project platform. The model user data analysis complex developed by the authors will simplify the process of managing various web projects during crises.

Multimodal Analysis of Video Collections: Visual Exploration of Presentation Techniques in TED Talks

While much research in the educational field has revealed many presentation techniques, they often overlap and are even occasionally contradictory. Exploring presentation techniques used in TED Talks could provide evidence for a practical guideline. This study aims to explore the verbal and non-verbal presentation techniques from a collection of TED Talks. However, such analysis is challenging due to the difficulties of analyzing multimodal video collections consisted of frame images, text, and metadata. This paper proposes a visual analytic system to analyze multimodal content in video collections. The system features three views at different levels: the Projection view with novel glyphs to facilitate cluster analysis regarding presentation styles; the Comparison View to present temporal distribution and concurrences of presentation techniques and support intra-cluster analysis; and the Video View to enable contextualized exploration of a video. We conduct a case study with language education experts and university students to provide anecdotal evidence about the effectiveness of our approach, and report new findings about presentation techniques in TED Talks. Quantitative feedback from a user study confirms the usefulness of our visual system for multimodal analysis of video collections.

Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-Operative Prostate Cancer

In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.

Visual Analytics of Heterogeneous Data Using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. These models represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach for the classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets. To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. We demonstrate the capability of our approach via two real-world cases.

VISTILES: Coordinating and Combining Co-located Mobile Devices for Visual Data Exploration

We present VISTILES, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype.