Yosuke Onoue

HyperInfo: Interactive Large Display for Informal Visual Communication

In an office or laboratory, the informal communication that suddenly occurs in a face-to-face meeting sometimes takes the form of a heated discussion or important information sharing. Visual information, such as pictures and graphs, facilitates the fast and accurate communication of an intention between people. Therefore, visual information should be shown quickly in this situation so that the conversation is not interrupted. This paper presents Hyper Info, a system that allows various types of content to be presented simultaneously. Hyper Info consists of a high-resolution large display, touch interaction device, and smartphone as a controller, and its operation is intuitive. The purpose of Hyper Info is to facilitate communication that utilizes visual information. Moreover, in Hyper Info an HTML rendering engine is applied that allows various types of content to be shown. We show that the advantages of a large high-resolution display are that some contents can be temporarily moved into a corner so that they do not overlap other contents and that contents can be expanded if necessary.

Layered Graph Drawing for Visualizing Evaluation Structures

An evaluation structure is a hierarchical structure of human cognition extracted from interviews based on the evaluation grid method. An evaluation structure can be defined as a directed acyclic graph (DAG). The authors propose a layer-assignment method that is part of the Sugiyama framework, a popular method for drawing DAGs, to satisfy the requirements for drawing evaluation structures. Their evaluations demonstrate that the layered graph drawing produced by the proposed layer-assignment method is preferred by users and aids in the understanding of evaluation structures.

E-Grid: a visual analytics system for evaluation structures

In this paper, we introduce E-Grid, a visual analytics system to aid in the understanding of human cognitive structures. E-Grid supports an evaluation structure that is a type of cognitive structure extracted using the evaluation grid method (EGM), which is a qualitative research method based on semi-structured interviews. The EGM is used to clarify user requirements in several fields of applications, such as environmental psychology, marketing research, and decision support. The importance of understanding user requirements is increasing in modern society because of the diversity of individual’s senses of values. E-Grid is designed to satisfy the requirements of EGM experts. In E-Grid, graph drawing and network analysis techniques are employed and users can explore an evaluation structure with the support of visual analytics techniques. The performance of E-Grid was evaluated in a case study using real data and feedback from EGM experts. Through this evaluation, it is demonstrated that the features of E-Grid make it an efficient and effective tool for the analysis of evaluation structures.Graphical Abstract

Minimizing the Number of Edges via Edge Concentration in Dense Layered Graphs

Edge concentration in dense bipartite graphs is a technique for reducing the numbers of edges and edge crossings in graph drawings. The conventional method proposed by Newbery is designed to reduce the number of edge crossings; however, it does not always reduce the number of edges. Reducing the number of edges is also an important factor for improving the readability of graphs. However, no edge concentration method with the explicit purpose of minimizing the number of edges has previously been studied. In this study, we propose a novel, efficient heuristic method for minimizing the number of edges during edge concentration. We demonstrate the efficiency of the proposed method via a comparison using randomly generated graphs. We find that Newberys method fails to reduce the number of edges when the number of vertices is large. By contrast, the proposed method achieves an average compression ratio of 47 to 82 percent for all generated graph groups. We also present a real-world application of the proposed method using a causality network of biological data.

A visual analytics system to support the formation of a hypothesis from calcium wave data

Abstract In most species, calcium waves in the oocyte are considered common phenomena in the activation of eggs. However, the mechanism of calcium waves has not yet been clarified. By collaborating with biologists studying Caenorhabditis elegans (C. elegans), which is widely used as a model organism, we observed that the following requirements must be satisfied to form a useful hypothesis based on calcium waves captured using high-speed in vivo imaging: (1) the ability to obtain an overview of how the calcium waves are propagated and (2) the ability to understand the propagation of waves in a narrow region. However, conventional visualization methods cannot satisfy these requirements simultaneously. Therefore, we propose a visual analytics system that allows users to understand and explore calcium wave images using cross-correlation analysis of the time-series data of the Ca2+ fluorescence intensity at each point. The interface of this system comprises an overview visualization, a detail visualization, and user interactions to satisfy these requirements and realize exploratory visualization. Some views present an overview visualization that displays the clustering results of a directed graph calculated using cross-correlation analysis. These views enable the users to understand the overview of wave propagation, thereby helping users find a region of interest. The detail visualization shows the relationship between the region of interest and other areas. Furthermore, users can use the proposed system with overview-detail and brush-link exploration to assign meaning to the region of interest and construct a hypothesis for its role. In this paper, we demonstrate how the proposed visual analytics approach works and how new hypotheses can be formed using the analysis of C. elegans calcium waves.

Visualization of JOV abstracts

Since the abstract can be found at the beginning of most scientific articles and is an essential part of the article, several attempts have been made to explore the rhetorical moves of abstracts in various research fields. These studies dealt only with accepted articles since they can be easily accessed. Although the findings of such works have some pedagogical implications for academic writing courses for young researchers who are relatively new to their fields, they do not contribute enough to the transparency of the peer review processes conducted in research fields. Increasing transparency requires considering rejected articles since they help to clarify the decision criteria in the peer review. Based on 591 abstracts of accepted or rejected articles submitted to Journal of Visualization (JOV), the present study aimed at exploring the differences between the accepted and rejected abstracts. The results show that there are significant differences in the structures of the abstracts. Since we also successfully develop a classification model for the decision using a machine-learning technique, the findings of this study have some implications for developing a semi-automatic reviewing system that can reduce the reviewer’s burden and increase the review quality.Graphical abstract

Optimal tree reordering for group-in-a-box graph layouts

Visualizing the group structure of graphs is important in analyzing complex networks. The group structure referred to here includes not only community structures defined in terms of modularity and the like but also group divisions based on node attributes. Group-In-a-Box (GIB) is a graph-drawing method designed for visualizing the group structure of graphs. Using a GIB layout, it is possible to simultaneously visualize group sizes and both within-group and between-group structures. However, conventional GIB layouts do not optimize display of between-group relations, causing many long edges to appear in the graph area and potentially reducing graph readability. This paper focuses on the tree structure of treemap used in GIB layouts as a basis for proposing a tree-reordered GIB (TRGIB) layout with a procedure for replacing sibling nodes in the tree structure. Group proximity is defined in terms of between-group distances and connection weights, and an optimal tree reordering problem (OTRP) that minimizes group proximity is formulated as a mixed-integer linear programming (MILP) problem. Through computational experiments, we show that optimal layout generation is possible in practical time by solving the OTRP using a general mathematical programming solver.

Quasi-biclique edge concentration: A visual analytics method for biclustering

Biclustering is a well-known approach for data mining, and it is applied in many fields, such as genome analyses, security services, and social network analyses. Biclustering finds bicliques contained in a bipartite graph. However, in real data, a biclique may lack several edges because of various reasons, such as errors. In this situation, traditional biclustering methods cannot find correct biclusters. A novel biclustering method that can analyze real data under uncertainty is needed. Quasi-biclique is a mathematical concept that represents incomplete bicliques. We propose the quasi-biclique edge concentration (QBEC) method, which is a visual analysis method for biclustering using quasi-biclique mining. QBEC includes visual representations and user interactions for quasi-bicliques. Quasi-bicliques contained in a bipartite graph are represented based on edge concentration. The incompleteness of a quasi-biclique is reflected in edge opacity. Users can interactively explore data by adjusting the incompleteness parameter of the quasi-biclique. We demonstrate the effectiveness of QBEC using real-world data.