The usability of virtual reality (VR) training applications is crucial for their success, but examining the usability in the early development stages remains challenging. A realistic and plausible solution would be revisiting and reconciling Heuristics Evaluation (HE) methods among the most widely used usability inspection methods in the human-computer interaction (HCI) domain. While research on studying and using HE methods is growing within the VR domain, few studies have considered the novel VR environment challenges new requirements for fitting HE methods to the context and applying them effectively. To this end, we conducted a user study with 14 evaluators using the standard HE methods to complete two HE sessions for a VR training application. We identified five critical challenges that evaluators encountered in the HE process by observing and interviewing them. Based on our findings, we discuss the importance of considering an easy-to-use heuristic set, how we can facilitate the HE procedures in the VR context, and the opportunities for developing HE-supporting tools.

The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap, a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments' corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap, heatmap, and dot distribution map. Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently; 2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping; and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds' active regions in a nature preserve.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.

We present an immersive virtual reality training system cryoVR with passive haptic feedback for training biological scientists preparing bio-sample for Cryo-Electron Microscopy (CryoEM). CryoEM requires the user to conduct careful operations on expensive delicate equipment. To minimize the risk and interruption of crucial research work, we tried to mimic the real lab using VR to let trainer practice in a virtual environment. We used 3D printed objects to provide passive haptic feedback in order to achieve a more realistic training experience. Participants are able to interact with the equipment in the virtual environment by moving or touching physical models. We developed all the necessary operations with haptic feedback, including moving, clicking, pouring, rotating, polling and pushing. By following instructions provided by our virtual reality simulator and interacting with physical objects, trainees will learn how to operate CryoEM with low cost and risk. By developing our training system, we explore the benefits, limitations and precautions of embedding haptic feedback to scientific VR training.

TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples.

In the face of increased cyber risks, we present our iterative design process and the resulting principles of SpringRain, an information visualization (infoVis) display design concept for large screens in network control rooms (NOCs). It aims to raise team situation awareness by visualizing large-scale multidimensional computer network data sets as a live “rainfall.” We used aesthetic principles and theories of perception to prototype this ambient, yet data-dense, visualization. By applying multiple data dimensions to different properties of a line segment, such as length, motion, and color, the two-dimensional visualization offers analytical affordances (i.e., graphic qualities that make it clear how the display should be “read”) that can be processed pre-attentively (i.e., under 200 ms). This grants that time-sensitive anomalies in the network can be noticed, processed, and addressed in a timely manner. Our design approach was driven by theories rather than existing design works in the hope of encouraging more user-centered theories-driven infoVis designs that are better suited to user needs and requirements. Expert reviewers’ feedback from the VAST 2013 Challenge committee confirmed the novelty of the design and pointed to opportunities for improvements. We discuss three redesign attempts to address some of the identified limitations.

We introduce a propagation-based parametric symbolic model approach to supporting analytic provenance. This approach combines a script language to capture and encode the analytic process and a parametrically controlled symbolic model to represent and reuse the logic of the analysis process. Our approach first appeared in a visual analytics system called CZSaw. Using a script to capture the analyst’s interactions at a meaningful system action level allows the creation of a parametrically controlled symbolic model in the form of a Directed Acyclic Graph (DAG). Using the DAG allows propagating changes. Graph nodes correspond to variables in CZSaw scripts, which are results (data and data visualizations) generated from user interactions. The user interacts with variables representing entities or relations to create the next step’s results. Graph edges represent dependency relationships among nodes. Any change to a variable triggers the propagation mechanism to update downstream dependent variables and in turn updates data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the graph. We evaluated this symbolic model approach by solving three IEEE VAST Challenge contest problems (from IEEE VAST 2008, 2009, and 2010). In each of these challenges, the analyst first created a symbolic model to explore, understand, analyze, and solve a particular subproblem and then reused the model via its dependency graph propagation mechanism to solve similar subproblems. With the script and model, CZSaw supports the analytic provenance by capturing, encoding, and reusing the analysis process. The analyst can recall the chronological states of the analysis process with the CZSaw script and may interpret the underlying rationale of the analysis with the symbolic model.

This paper proposes an autolinking approach to help analysts investigate spatial details of suspicious sections from an overview temporal visualization. Analysis of spatial-temporal network security data takes place both conditionally and in sequence. Many systems use time-series curves to visualize the temporal perspectives of the data and maps to show the spatial information. To identify anomalies, the analysts frequently shift across different visualizations. In essence, time-series curves provide a temporal overview of data, and the map anchors the locations for the users to drill down for details. Anomalies may be reflected in a time-series curve as a jump, a dive, a peak, or a valley. With the autolinking mechanism, after the analyst selects a segment of a curve, the system can automatically highlight the related area on the map for further investigation. This approach adopts the slicing operation of the Online Analytical Process (OLAP) to find the basic granularities that contribute to the overall value change. This approach is implemented in our award-winning visual analytics system SemanticPrism. In this paper, we describe its structure and demonstrate three examples of use with the VAST 2012 Minichallenge 1 data.

This paper investigates the design collaboration process between the Interaction Design (IXD) program and Engineering Projects in Community Service (EPICS) program at Purdue University. There have been quite a few recent research focused on analyzing the collaboration between Industrial Design students and Engineering students. However, little in the literature has been dedicated to discuss the education of Interaction Design and its collaboration with engineering programs. In this research, we tried to identify characteristics that differentiate IXD and EPICS students by focusing on the experiences of the IXD students. Some characteristics made the collaboration successful, but other created frustrations. In order to understand, explore, and evaluate the collaboration process, we collected the data during and after the collaboration and qualitatively analyzed it using a design collaboration model. This analysis identified several interesting features of this interdisciplinary collaboration. The collaboration between two programs benefited both sides and will be conducted regularly. The understanding gained from this study helps to understand and manage the discourse during the interdisciplinary collaboration, which will benefit our future education and inspire better interdisciplinary education and collaboration.

This study identifies the areas of autism therapy that can be enhanced by using a touch screen mobile computer (TSMC) device to facilitate autistic children's learning experience. At an affordable price range, the simple touch screen of a TSMC has an immediate cause and effect response that enables these students to be more independent during the learning process. These key factors make the TSMC an ideal supplement for autism therapy in the classroom and for skill reinforcement at home. However, to identify a correct starting point is crucial for the success of this research. Our study aims to explore the most effective application design areas using a TSMC as an instructional tool for autistic children. To determine this direction, four main steps were identified. These steps include reviewing current literature on autism deficits, evaluating the interaction design of the TSMC, analyzing existing TSMC applications currently available, and designing a prototype application for evaluation. This paper reports our model of information processing interruption in autism learning, our target zones of instruction based on the literature review of autism deficits, and introduces our implementation plan of the application design to enhance autism therapy.

No abstract available.

The NEAR (navigating exhibitions, annotations and resources) panel is a method of managing digital collections and user preferences through collaborative filtering and graphically revealing implicit data relations such as sharing, reference and similarity. It is implemented on AldrVIldrRE, an online multimedia repository. AldrVIldrRE supports semi-structured collections (exhibitions) which containing various resources and annotations. Its users are encouraged to contribute, share, annotate and interpret resources. Similar to the act of adding items into shopping carts in the e-commence applications, a user's activities of searching, organizing and interpreting data in AldrVIldrRE are considered as evidence of user's preferences. The design process of NEAR was guided by several principles from the visualization literature. It implements new navigation and communication approaches that support discovery of relations. Having tested NEAR with several users, we further analyze the design, report the evaluation and consider its use in other applications.